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    • org.tensorflow.op.Ops Maven / Gradle / Ivy

    package org.tensorflow.op;

import java.nio.ByteBuffer;
import java.nio.DoubleBuffer;
import java.nio.FloatBuffer;
import java.nio.IntBuffer;
import java.nio.LongBuffer;
import java.util.List;
import org.tensorflow.Graph;
import org.tensorflow.Operand;
import org.tensorflow.Shape;
import org.tensorflow.op.core.Abort;
import org.tensorflow.op.core.Abs;
import org.tensorflow.op.core.AccumulateNV2;
import org.tensorflow.op.core.AccumulatorApplyGradient;
import org.tensorflow.op.core.AccumulatorNumAccumulated;
import org.tensorflow.op.core.AccumulatorSetGlobalStep;
import org.tensorflow.op.core.AccumulatorTakeGradient;
import org.tensorflow.op.core.Acos;
import org.tensorflow.op.core.Acosh;
import org.tensorflow.op.core.Add;
import org.tensorflow.op.core.AddManySparseToTensorsMap;
import org.tensorflow.op.core.AddN;
import org.tensorflow.op.core.AddSparseToTensorsMap;
import org.tensorflow.op.core.AddV2;
import org.tensorflow.op.core.AdjustContrast;
import org.tensorflow.op.core.AdjustHue;
import org.tensorflow.op.core.AdjustSaturation;
import org.tensorflow.op.core.All;
import org.tensorflow.op.core.AllCandidateSampler;
import org.tensorflow.op.core.Angle;
import org.tensorflow.op.core.AnonymousIterator;
import org.tensorflow.op.core.Any;
import org.tensorflow.op.core.ApplyAdadelta;
import org.tensorflow.op.core.ApplyAdagrad;
import org.tensorflow.op.core.ApplyAdagradDA;
import org.tensorflow.op.core.ApplyAdam;
import org.tensorflow.op.core.ApplyAddSign;
import org.tensorflow.op.core.ApplyCenteredRMSProp;
import org.tensorflow.op.core.ApplyFtrl;
import org.tensorflow.op.core.ApplyFtrlV2;
import org.tensorflow.op.core.ApplyGradientDescent;
import org.tensorflow.op.core.ApplyMomentum;
import org.tensorflow.op.core.ApplyPowerSign;
import org.tensorflow.op.core.ApplyProximalAdagrad;
import org.tensorflow.op.core.ApplyProximalGradientDescent;
import org.tensorflow.op.core.ApplyRMSProp;
import org.tensorflow.op.core.ApproximateEqual;
import org.tensorflow.op.core.ArgMax;
import org.tensorflow.op.core.ArgMin;
import org.tensorflow.op.core.AsString;
import org.tensorflow.op.core.Asin;
import org.tensorflow.op.core.Asinh;
import org.tensorflow.op.core.Assign;
import org.tensorflow.op.core.AssignAdd;
import org.tensorflow.op.core.AssignAddVariableOp;
import org.tensorflow.op.core.AssignSub;
import org.tensorflow.op.core.AssignSubVariableOp;
import org.tensorflow.op.core.AssignVariableOp;
import org.tensorflow.op.core.Atan;
import org.tensorflow.op.core.Atan2;
import org.tensorflow.op.core.Atanh;
import org.tensorflow.op.core.AudioSpectrogram;
import org.tensorflow.op.core.AudioSummary;
import org.tensorflow.op.core.AvgPool;
import org.tensorflow.op.core.AvgPool3D;
import org.tensorflow.op.core.AvgPool3DGrad;
import org.tensorflow.op.core.Barrier;
import org.tensorflow.op.core.BarrierClose;
import org.tensorflow.op.core.BarrierIncompleteSize;
import org.tensorflow.op.core.BarrierInsertMany;
import org.tensorflow.op.core.BarrierReadySize;
import org.tensorflow.op.core.BarrierTakeMany;
import org.tensorflow.op.core.Batch;
import org.tensorflow.op.core.BatchCholesky;
import org.tensorflow.op.core.BatchCholeskyGrad;
import org.tensorflow.op.core.BatchDataset;
import org.tensorflow.op.core.BatchFFT;
import org.tensorflow.op.core.BatchFFT2D;
import org.tensorflow.op.core.BatchFFT3D;
import org.tensorflow.op.core.BatchIFFT;
import org.tensorflow.op.core.BatchIFFT2D;
import org.tensorflow.op.core.BatchIFFT3D;
import org.tensorflow.op.core.BatchMatMul;
import org.tensorflow.op.core.BatchMatrixBandPart;
import org.tensorflow.op.core.BatchMatrixDeterminant;
import org.tensorflow.op.core.BatchMatrixDiag;
import org.tensorflow.op.core.BatchMatrixDiagPart;
import org.tensorflow.op.core.BatchMatrixInverse;
import org.tensorflow.op.core.BatchMatrixSetDiag;
import org.tensorflow.op.core.BatchMatrixSolve;
import org.tensorflow.op.core.BatchMatrixSolveLs;
import org.tensorflow.op.core.BatchMatrixTriangularSolve;
import org.tensorflow.op.core.BatchNormWithGlobalNormalization;
import org.tensorflow.op.core.BatchNormWithGlobalNormalizationGrad;
import org.tensorflow.op.core.BatchSelfAdjointEig;
import org.tensorflow.op.core.BatchSelfAdjointEigV2;
import org.tensorflow.op.core.BatchSvd;
import org.tensorflow.op.core.BatchToSpace;
import org.tensorflow.op.core.BatchToSpaceND;
import org.tensorflow.op.core.BesselI0e;
import org.tensorflow.op.core.BesselI1e;
import org.tensorflow.op.core.Betainc;
import org.tensorflow.op.core.BiasAdd;
import org.tensorflow.op.core.BiasAddGrad;
import org.tensorflow.op.core.BigQueryReader;
import org.tensorflow.op.core.Bincount;
import org.tensorflow.op.core.Bitcast;
import org.tensorflow.op.core.BitwiseAnd;
import org.tensorflow.op.core.BitwiseOr;
import org.tensorflow.op.core.BitwiseXor;
import org.tensorflow.op.core.BroadcastDynamicShape;
import org.tensorflow.op.core.BroadcastTo;
import org.tensorflow.op.core.Bucketize;
import org.tensorflow.op.core.BytesProducedStatsDataset;
import org.tensorflow.op.core.CTCBeamSearchDecoder;
import org.tensorflow.op.core.CTCGreedyDecoder;
import org.tensorflow.op.core.CTCLoss;
import org.tensorflow.op.core.CacheDataset;
import org.tensorflow.op.core.Cast;
import org.tensorflow.op.core.Ceil;
import org.tensorflow.op.core.CheckNumerics;
import org.tensorflow.op.core.Cholesky;
import org.tensorflow.op.core.CholeskyGrad;
import org.tensorflow.op.core.ClipByValue;
import org.tensorflow.op.core.CompareAndBitpack;
import org.tensorflow.op.core.Complex;
import org.tensorflow.op.core.ComplexAbs;
import org.tensorflow.op.core.ComputeAccidentalHits;
import org.tensorflow.op.core.Concat;
import org.tensorflow.op.core.ConcatenateDataset;
import org.tensorflow.op.core.ConditionalAccumulator;
import org.tensorflow.op.core.Conj;
import org.tensorflow.op.core.ConjugateTranspose;
import org.tensorflow.op.core.Constant;
import org.tensorflow.op.core.ConsumeMutexLock;
import org.tensorflow.op.core.ControlTrigger;
import org.tensorflow.op.core.Conv2D;
import org.tensorflow.op.core.Conv2DBackpropFilter;
import org.tensorflow.op.core.Conv2DBackpropInput;
import org.tensorflow.op.core.Conv3D;
import org.tensorflow.op.core.Conv3DBackpropFilter;
import org.tensorflow.op.core.Conv3DBackpropFilterV2;
import org.tensorflow.op.core.Conv3DBackpropInput;
import org.tensorflow.op.core.Conv3DBackpropInputV2;
import org.tensorflow.op.core.Cos;
import org.tensorflow.op.core.Cosh;
import org.tensorflow.op.core.CountUpTo;
import org.tensorflow.op.core.CropAndResize;
import org.tensorflow.op.core.CropAndResizeGradBoxes;
import org.tensorflow.op.core.CropAndResizeGradImage;
import org.tensorflow.op.core.Cross;
import org.tensorflow.op.core.CudnnRNN;
import org.tensorflow.op.core.CudnnRNNBackprop;
import org.tensorflow.op.core.CudnnRNNCanonicalToParams;
import org.tensorflow.op.core.CudnnRNNParamsSize;
import org.tensorflow.op.core.CudnnRNNParamsToCanonical;
import org.tensorflow.op.core.Cumprod;
import org.tensorflow.op.core.Cumsum;
import org.tensorflow.op.core.DataFormatDimMap;
import org.tensorflow.op.core.DataFormatVecPermute;
import org.tensorflow.op.core.DatasetToSingleElement;
import org.tensorflow.op.core.DebugGradientIdentity;
import org.tensorflow.op.core.DebugGradientRefIdentity;
import org.tensorflow.op.core.DecodeAndCropJpeg;
import org.tensorflow.op.core.DecodeBase64;
import org.tensorflow.op.core.DecodeBmp;
import org.tensorflow.op.core.DecodeCSV;
import org.tensorflow.op.core.DecodeCompressed;
import org.tensorflow.op.core.DecodeGif;
import org.tensorflow.op.core.DecodeJSONExample;
import org.tensorflow.op.core.DecodeJpeg;
import org.tensorflow.op.core.DecodePng;
import org.tensorflow.op.core.DecodeProtoV2;
import org.tensorflow.op.core.DecodeRaw;
import org.tensorflow.op.core.DecodeWav;
import org.tensorflow.op.core.DeepCopy;
import org.tensorflow.op.core.DeleteSessionTensor;
import org.tensorflow.op.core.DenseToDenseSetOperation;
import org.tensorflow.op.core.DenseToSparseBatchDataset;
import org.tensorflow.op.core.DenseToSparseSetOperation;
import org.tensorflow.op.core.DepthToSpace;
import org.tensorflow.op.core.DepthwiseConv2dNative;
import org.tensorflow.op.core.DepthwiseConv2dNativeBackpropFilter;
import org.tensorflow.op.core.DepthwiseConv2dNativeBackpropInput;
import org.tensorflow.op.core.Dequantize;
import org.tensorflow.op.core.DeserializeIterator;
import org.tensorflow.op.core.DeserializeManySparse;
import org.tensorflow.op.core.DeserializeSparse;
import org.tensorflow.op.core.DestroyResourceOp;
import org.tensorflow.op.core.DestroyTemporaryVariable;
import org.tensorflow.op.core.Diag;
import org.tensorflow.op.core.DiagPart;
import org.tensorflow.op.core.Digamma;
import org.tensorflow.op.core.Dilation2D;
import org.tensorflow.op.core.Dilation2DBackpropFilter;
import org.tensorflow.op.core.Dilation2DBackpropInput;
import org.tensorflow.op.core.Div;
import org.tensorflow.op.core.DrawBoundingBoxes;
import org.tensorflow.op.core.DynamicPartition;
import org.tensorflow.op.core.DynamicStitch;
import org.tensorflow.op.core.EagerPyFunc;
import org.tensorflow.op.core.EditDistance;
import org.tensorflow.op.core.Elu;
import org.tensorflow.op.core.Empty;
import org.tensorflow.op.core.EmptyTensorList;
import org.tensorflow.op.core.EncodeBase64;
import org.tensorflow.op.core.EncodeJpeg;
import org.tensorflow.op.core.EncodePng;
import org.tensorflow.op.core.EncodeProto;
import org.tensorflow.op.core.EncodeWav;
import org.tensorflow.op.core.EnqueueInQueueDataset;
import org.tensorflow.op.core.Equal;
import org.tensorflow.op.core.Erf;
import org.tensorflow.op.core.Erfc;
import org.tensorflow.op.core.Exp;
import org.tensorflow.op.core.ExpandDims;
import org.tensorflow.op.core.Expm1;
import org.tensorflow.op.core.ExtractGlimpse;
import org.tensorflow.op.core.ExtractImagePatches;
import org.tensorflow.op.core.ExtractJpegShape;
import org.tensorflow.op.core.FFT;
import org.tensorflow.op.core.FFT2D;
import org.tensorflow.op.core.FFT3D;
import org.tensorflow.op.core.FIFOQueue;
import org.tensorflow.op.core.Fact;
import org.tensorflow.op.core.FakeQuantWithMinMaxArgs;
import org.tensorflow.op.core.FakeQuantWithMinMaxArgsGradient;
import org.tensorflow.op.core.FakeQuantWithMinMaxVars;
import org.tensorflow.op.core.FakeQuantWithMinMaxVarsGradient;
import org.tensorflow.op.core.FakeQuantWithMinMaxVarsPerChannel;
import org.tensorflow.op.core.FakeQuantWithMinMaxVarsPerChannelGradient;
import org.tensorflow.op.core.FeatureStatsDataset;
import org.tensorflow.op.core.Fill;
import org.tensorflow.op.core.FixedLengthRecordDataset;
import org.tensorflow.op.core.FixedLengthRecordReader;
import org.tensorflow.op.core.FixedUnigramCandidateSampler;
import org.tensorflow.op.core.Floor;
import org.tensorflow.op.core.FloorDiv;
import org.tensorflow.op.core.FloorMod;
import org.tensorflow.op.core.FractionalAvgPool;
import org.tensorflow.op.core.FractionalMaxPool;
import org.tensorflow.op.core.FusedBatchNorm;
import org.tensorflow.op.core.FusedBatchNormGrad;
import org.tensorflow.op.core.FusedBatchNormGradV2;
import org.tensorflow.op.core.FusedBatchNormV2;
import org.tensorflow.op.core.FusedPadConv2D;
import org.tensorflow.op.core.FusedResizeAndPadConv2D;
import org.tensorflow.op.core.Gather;
import org.tensorflow.op.core.GatherNd;
import org.tensorflow.op.core.GatherV2;
import org.tensorflow.op.core.GcsConfigureBlockCache;
import org.tensorflow.op.core.GcsConfigureCredentials;
import org.tensorflow.op.core.GenerateBigQueryReaderPartitions;
import org.tensorflow.op.core.GenerateVocabRemapping;
import org.tensorflow.op.core.GetSessionHandle;
import org.tensorflow.op.core.GetSessionHandleV2;
import org.tensorflow.op.core.GetSessionTensor;
import org.tensorflow.op.core.Gradients;
import org.tensorflow.op.core.Greater;
import org.tensorflow.op.core.GreaterEqual;
import org.tensorflow.op.core.GuaranteeConst;
import org.tensorflow.op.core.HSVToRGB;
import org.tensorflow.op.core.HashTable;
import org.tensorflow.op.core.HistogramFixedWidth;
import org.tensorflow.op.core.HistogramSummary;
import org.tensorflow.op.core.IFFT;
import org.tensorflow.op.core.IFFT2D;
import org.tensorflow.op.core.IFFT3D;
import org.tensorflow.op.core.IRFFT;
import org.tensorflow.op.core.IRFFT2D;
import org.tensorflow.op.core.IRFFT3D;
import org.tensorflow.op.core.Identity;
import org.tensorflow.op.core.IdentityN;
import org.tensorflow.op.core.IdentityReader;
import org.tensorflow.op.core.Igamma;
import org.tensorflow.op.core.Igammac;
import org.tensorflow.op.core.Imag;
import org.tensorflow.op.core.ImageSummary;
import org.tensorflow.op.core.ImmutableConst;
import org.tensorflow.op.core.InTopK;
import org.tensorflow.op.core.InTopKV2;
import org.tensorflow.op.core.InitializeTable;
import org.tensorflow.op.core.InitializeTableFromTextFile;
import org.tensorflow.op.core.InplaceAdd;
import org.tensorflow.op.core.InplaceSub;
import org.tensorflow.op.core.InplaceUpdate;
import org.tensorflow.op.core.Inv;
import org.tensorflow.op.core.Invert;
import org.tensorflow.op.core.InvertPermutation;
import org.tensorflow.op.core.IsFinite;
import org.tensorflow.op.core.IsInf;
import org.tensorflow.op.core.IsNan;
import org.tensorflow.op.core.IsVariableInitialized;
import org.tensorflow.op.core.Iterator;
import org.tensorflow.op.core.IteratorFromStringHandle;
import org.tensorflow.op.core.IteratorGetNext;
import org.tensorflow.op.core.IteratorGetNextSync;
import org.tensorflow.op.core.IteratorToStringHandle;
import org.tensorflow.op.core.L2Loss;
import org.tensorflow.op.core.LMDBReader;
import org.tensorflow.op.core.LRN;
import org.tensorflow.op.core.LatencyStatsDataset;
import org.tensorflow.op.core.LearnedUnigramCandidateSampler;
import org.tensorflow.op.core.LeftShift;
import org.tensorflow.op.core.Less;
import org.tensorflow.op.core.LessEqual;
import org.tensorflow.op.core.Lgamma;
import org.tensorflow.op.core.LinSpace;
import org.tensorflow.op.core.LoadAndRemapMatrix;
import org.tensorflow.op.core.Log;
import org.tensorflow.op.core.Log1p;
import org.tensorflow.op.core.LogMatrixDeterminant;
import org.tensorflow.op.core.LogSoftmax;
import org.tensorflow.op.core.LogUniformCandidateSampler;
import org.tensorflow.op.core.LogicalAnd;
import org.tensorflow.op.core.LogicalNot;
import org.tensorflow.op.core.LogicalOr;
import org.tensorflow.op.core.LookupTableExport;
import org.tensorflow.op.core.LookupTableFind;
import org.tensorflow.op.core.LookupTableImport;
import org.tensorflow.op.core.LookupTableInsert;
import org.tensorflow.op.core.LookupTableSize;
import org.tensorflow.op.core.LoopCond;
import org.tensorflow.op.core.MakeIterator;
import org.tensorflow.op.core.MapClear;
import org.tensorflow.op.core.MapIncompleteSize;
import org.tensorflow.op.core.MapPeek;
import org.tensorflow.op.core.MapSize;
import org.tensorflow.op.core.MapStage;
import org.tensorflow.op.core.MapUnstage;
import org.tensorflow.op.core.MapUnstageNoKey;
import org.tensorflow.op.core.MatMul;
import org.tensorflow.op.core.MatchingFiles;
import org.tensorflow.op.core.MatrixBandPart;
import org.tensorflow.op.core.MatrixDeterminant;
import org.tensorflow.op.core.MatrixDiag;
import org.tensorflow.op.core.MatrixDiagPart;
import org.tensorflow.op.core.MatrixExponential;
import org.tensorflow.op.core.MatrixInverse;
import org.tensorflow.op.core.MatrixSetDiag;
import org.tensorflow.op.core.MatrixSolve;
import org.tensorflow.op.core.MatrixSolveLs;
import org.tensorflow.op.core.MatrixTriangularSolve;
import org.tensorflow.op.core.Max;
import org.tensorflow.op.core.MaxPool;
import org.tensorflow.op.core.MaxPool3D;
import org.tensorflow.op.core.MaxPool3DGrad;
import org.tensorflow.op.core.MaxPool3DGradGrad;
import org.tensorflow.op.core.MaxPoolGradGrad;
import org.tensorflow.op.core.MaxPoolGradGradV2;
import org.tensorflow.op.core.MaxPoolGradGradWithArgmax;
import org.tensorflow.op.core.MaxPoolGradV2;
import org.tensorflow.op.core.MaxPoolV2;
import org.tensorflow.op.core.MaxPoolWithArgmax;
import org.tensorflow.op.core.Maximum;
import org.tensorflow.op.core.Mean;
import org.tensorflow.op.core.Merge;
import org.tensorflow.op.core.MergeSummary;
import org.tensorflow.op.core.MergeV2Checkpoints;
import org.tensorflow.op.core.Mfcc;
import org.tensorflow.op.core.Min;
import org.tensorflow.op.core.Minimum;
import org.tensorflow.op.core.MirrorPad;
import org.tensorflow.op.core.Mod;
import org.tensorflow.op.core.Mul;
import org.tensorflow.op.core.Multinomial;
import org.tensorflow.op.core.Multiply;
import org.tensorflow.op.core.MutableDenseHashTable;
import org.tensorflow.op.core.MutableHashTable;
import org.tensorflow.op.core.MutableHashTableOfTensors;
import org.tensorflow.op.core.MutexLock;
import org.tensorflow.op.core.MutexV2;
import org.tensorflow.op.core.Neg;
import org.tensorflow.op.core.NegTrain;
import org.tensorflow.op.core.Negate;
import org.tensorflow.op.core.NextIteration;
import org.tensorflow.op.core.NoOp;
import org.tensorflow.op.core.NonMaxSuppression;
import org.tensorflow.op.core.NonMaxSuppressionV2;
import org.tensorflow.op.core.NonMaxSuppressionV3;
import org.tensorflow.op.core.NonMaxSuppressionWithOverlaps;
import org.tensorflow.op.core.NotEqual;
import org.tensorflow.op.core.NthElement;
import org.tensorflow.op.core.OneHot;
import org.tensorflow.op.core.OnesLike;
import org.tensorflow.op.core.OrderedMapClear;
import org.tensorflow.op.core.OrderedMapIncompleteSize;
import org.tensorflow.op.core.OrderedMapPeek;
import org.tensorflow.op.core.OrderedMapSize;
import org.tensorflow.op.core.OrderedMapStage;
import org.tensorflow.op.core.OrderedMapUnstage;
import org.tensorflow.op.core.OrderedMapUnstageNoKey;
import org.tensorflow.op.core.Pad;
import org.tensorflow.op.core.PadV2;
import org.tensorflow.op.core.PaddedBatchDataset;
import org.tensorflow.op.core.PaddingFIFOQueue;
import org.tensorflow.op.core.ParallelConcat;
import org.tensorflow.op.core.ParallelDynamicStitch;
import org.tensorflow.op.core.ParameterizedTruncatedNormal;
import org.tensorflow.op.core.ParseExample;
import org.tensorflow.op.core.ParseSingleExample;
import org.tensorflow.op.core.ParseSingleSequenceExample;
import org.tensorflow.op.core.ParseTensor;
import org.tensorflow.op.core.Placeholder;
import org.tensorflow.op.core.PlaceholderV2;
import org.tensorflow.op.core.PlaceholderWithDefault;
import org.tensorflow.op.core.Polygamma;
import org.tensorflow.op.core.PopulationCount;
import org.tensorflow.op.core.Pow;
import org.tensorflow.op.core.PrefetchDataset;
import org.tensorflow.op.core.PrependFromQueueAndPaddedBatchDataset;
import org.tensorflow.op.core.PreventGradient;
import org.tensorflow.op.core.Print;
import org.tensorflow.op.core.PriorityQueue;
import org.tensorflow.op.core.Prod;
import org.tensorflow.op.core.Qr;
import org.tensorflow.op.core.QuantizeAndDequantize;
import org.tensorflow.op.core.QuantizeAndDequantizeV2;
import org.tensorflow.op.core.QuantizeAndDequantizeV3;
import org.tensorflow.op.core.QuantizeDownAndShrinkRange;
import org.tensorflow.op.core.QuantizeV2;
import org.tensorflow.op.core.QuantizedAdd;
import org.tensorflow.op.core.QuantizedAvgPool;
import org.tensorflow.op.core.QuantizedBatchNormWithGlobalNormalization;
import org.tensorflow.op.core.QuantizedBiasAdd;
import org.tensorflow.op.core.QuantizedConcat;
import org.tensorflow.op.core.QuantizedConv2D;
import org.tensorflow.op.core.QuantizedInstanceNorm;
import org.tensorflow.op.core.QuantizedMatMul;
import org.tensorflow.op.core.QuantizedMaxPool;
import org.tensorflow.op.core.QuantizedMul;
import org.tensorflow.op.core.QuantizedRelu;
import org.tensorflow.op.core.QuantizedRelu6;
import org.tensorflow.op.core.QuantizedReluX;
import org.tensorflow.op.core.QuantizedReshape;
import org.tensorflow.op.core.QuantizedResizeBilinear;
import org.tensorflow.op.core.QueueClose;
import org.tensorflow.op.core.QueueDequeue;
import org.tensorflow.op.core.QueueDequeueMany;
import org.tensorflow.op.core.QueueDequeueUpTo;
import org.tensorflow.op.core.QueueEnqueue;
import org.tensorflow.op.core.QueueEnqueueMany;
import org.tensorflow.op.core.QueueIsClosed;
import org.tensorflow.op.core.QueueIsClosedV2;
import org.tensorflow.op.core.QueueSize;
import org.tensorflow.op.core.RFFT;
import org.tensorflow.op.core.RFFT2D;
import org.tensorflow.op.core.RFFT3D;
import org.tensorflow.op.core.RGBToHSV;
import org.tensorflow.op.core.RandomCrop;
import org.tensorflow.op.core.RandomDataset;
import org.tensorflow.op.core.RandomGamma;
import org.tensorflow.op.core.RandomNormal;
import org.tensorflow.op.core.RandomPoisson;
import org.tensorflow.op.core.RandomPoissonV2;
import org.tensorflow.op.core.RandomShuffle;
import org.tensorflow.op.core.RandomShuffleQueue;
import org.tensorflow.op.core.RandomUniform;
import org.tensorflow.op.core.RandomUniformInt;
import org.tensorflow.op.core.Range;
import org.tensorflow.op.core.RangeDataset;
import org.tensorflow.op.core.Rank;
import org.tensorflow.op.core.ReadFile;
import org.tensorflow.op.core.ReadVariableOp;
import org.tensorflow.op.core.ReaderNumRecordsProduced;
import org.tensorflow.op.core.ReaderNumWorkUnitsCompleted;
import org.tensorflow.op.core.ReaderRead;
import org.tensorflow.op.core.ReaderReadUpTo;
import org.tensorflow.op.core.ReaderReset;
import org.tensorflow.op.core.ReaderRestoreState;
import org.tensorflow.op.core.ReaderSerializeState;
import org.tensorflow.op.core.Real;
import org.tensorflow.op.core.RealDiv;
import org.tensorflow.op.core.Reciprocal;
import org.tensorflow.op.core.RecordInput;
import org.tensorflow.op.core.ReduceAll;
import org.tensorflow.op.core.ReduceAny;
import org.tensorflow.op.core.ReduceJoin;
import org.tensorflow.op.core.ReduceMax;
import org.tensorflow.op.core.ReduceMean;
import org.tensorflow.op.core.ReduceMin;
import org.tensorflow.op.core.ReduceProd;
import org.tensorflow.op.core.ReduceSum;
import org.tensorflow.op.core.RefNextIteration;
import org.tensorflow.op.core.RefSelect;
import org.tensorflow.op.core.RefSwitch;
import org.tensorflow.op.core.RegexFullMatch;
import org.tensorflow.op.core.RegexReplace;
import org.tensorflow.op.core.Relu;
import org.tensorflow.op.core.Relu6;
import org.tensorflow.op.core.RemoteFusedGraphExecute;
import org.tensorflow.op.core.RepeatDataset;
import org.tensorflow.op.core.RequantizationRange;
import org.tensorflow.op.core.Requantize;
import org.tensorflow.op.core.Reshape;
import org.tensorflow.op.core.ResizeArea;
import org.tensorflow.op.core.ResizeBicubic;
import org.tensorflow.op.core.ResizeBilinear;
import org.tensorflow.op.core.ResizeNearestNeighbor;
import org.tensorflow.op.core.ResourceApplyAdadelta;
import org.tensorflow.op.core.ResourceApplyAdagrad;
import org.tensorflow.op.core.ResourceApplyAdagradDA;
import org.tensorflow.op.core.ResourceApplyAdam;
import org.tensorflow.op.core.ResourceApplyAddSign;
import org.tensorflow.op.core.ResourceApplyCenteredRMSProp;
import org.tensorflow.op.core.ResourceApplyFtrl;
import org.tensorflow.op.core.ResourceApplyFtrlV2;
import org.tensorflow.op.core.ResourceApplyGradientDescent;
import org.tensorflow.op.core.ResourceApplyMomentum;
import org.tensorflow.op.core.ResourceApplyPowerSign;
import org.tensorflow.op.core.ResourceApplyProximalAdagrad;
import org.tensorflow.op.core.ResourceApplyProximalGradientDescent;
import org.tensorflow.op.core.ResourceApplyRMSProp;
import org.tensorflow.op.core.ResourceCountUpTo;
import org.tensorflow.op.core.ResourceGather;
import org.tensorflow.op.core.ResourceScatterAdd;
import org.tensorflow.op.core.ResourceScatterDiv;
import org.tensorflow.op.core.ResourceScatterMax;
import org.tensorflow.op.core.ResourceScatterMin;
import org.tensorflow.op.core.ResourceScatterMul;
import org.tensorflow.op.core.ResourceScatterNdAdd;
import org.tensorflow.op.core.ResourceScatterNdUpdate;
import org.tensorflow.op.core.ResourceScatterSub;
import org.tensorflow.op.core.ResourceScatterUpdate;
import org.tensorflow.op.core.ResourceSparseApplyAdadelta;
import org.tensorflow.op.core.ResourceSparseApplyAdagrad;
import org.tensorflow.op.core.ResourceSparseApplyAdagradDA;
import org.tensorflow.op.core.ResourceSparseApplyCenteredRMSProp;
import org.tensorflow.op.core.ResourceSparseApplyFtrl;
import org.tensorflow.op.core.ResourceSparseApplyFtrlV2;
import org.tensorflow.op.core.ResourceSparseApplyMomentum;
import org.tensorflow.op.core.ResourceSparseApplyProximalAdagrad;
import org.tensorflow.op.core.ResourceSparseApplyProximalGradientDescent;
import org.tensorflow.op.core.ResourceSparseApplyRMSProp;
import org.tensorflow.op.core.ResourceStridedSliceAssign;
import org.tensorflow.op.core.Restore;
import org.tensorflow.op.core.RestoreSlice;
import org.tensorflow.op.core.RestoreV2;
import org.tensorflow.op.core.Reverse;
import org.tensorflow.op.core.ReverseSequence;
import org.tensorflow.op.core.RightShift;
import org.tensorflow.op.core.Rint;
import org.tensorflow.op.core.Roll;
import org.tensorflow.op.core.Round;
import org.tensorflow.op.core.Rpc;
import org.tensorflow.op.core.Rsqrt;
import org.tensorflow.op.core.SampleDistortedBoundingBox;
import org.tensorflow.op.core.SampleDistortedBoundingBoxV2;
import org.tensorflow.op.core.Save;
import org.tensorflow.op.core.SaveSlices;
import org.tensorflow.op.core.SaveV2;
import org.tensorflow.op.core.ScalarSummary;
import org.tensorflow.op.core.ScatterAdd;
import org.tensorflow.op.core.ScatterDiv;
import org.tensorflow.op.core.ScatterMax;
import org.tensorflow.op.core.ScatterMin;
import org.tensorflow.op.core.ScatterMul;
import org.tensorflow.op.core.ScatterNd;
import org.tensorflow.op.core.ScatterNdAdd;
import org.tensorflow.op.core.ScatterNdNonAliasingAdd;
import org.tensorflow.op.core.ScatterNdSub;
import org.tensorflow.op.core.ScatterNdUpdate;
import org.tensorflow.op.core.ScatterSub;
import org.tensorflow.op.core.ScatterUpdate;
import org.tensorflow.op.core.SdcaFprint;
import org.tensorflow.op.core.SdcaOptimizer;
import org.tensorflow.op.core.SdcaShrinkL1;
import org.tensorflow.op.core.SegmentMax;
import org.tensorflow.op.core.SegmentMean;
import org.tensorflow.op.core.SegmentMin;
import org.tensorflow.op.core.SegmentProd;
import org.tensorflow.op.core.SegmentSum;
import org.tensorflow.op.core.SelfAdjointEig;
import org.tensorflow.op.core.Selu;
import org.tensorflow.op.core.SerializeIterator;
import org.tensorflow.op.core.SerializeManySparse;
import org.tensorflow.op.core.SerializeSparse;
import org.tensorflow.op.core.SerializeTensor;
import org.tensorflow.op.core.SetDiff1D;
import org.tensorflow.op.core.SetSize;
import org.tensorflow.op.core.SetStatsAggregatorDataset;
import org.tensorflow.op.core.ShapeN;
import org.tensorflow.op.core.ShardedFilename;
import org.tensorflow.op.core.ShardedFilespec;
import org.tensorflow.op.core.ShuffleAndRepeatDataset;
import org.tensorflow.op.core.ShuffleDataset;
import org.tensorflow.op.core.Sigmoid;
import org.tensorflow.op.core.Sign;
import org.tensorflow.op.core.Sin;
import org.tensorflow.op.core.Sinh;
import org.tensorflow.op.core.Size;
import org.tensorflow.op.core.SkipDataset;
import org.tensorflow.op.core.Skipgram;
import org.tensorflow.op.core.Slice;
import org.tensorflow.op.core.SlideDataset;
import org.tensorflow.op.core.Snapshot;
import org.tensorflow.op.core.Softmax;
import org.tensorflow.op.core.SoftmaxCrossEntropyWithLogits;
import org.tensorflow.op.core.Softplus;
import org.tensorflow.op.core.Softsign;
import org.tensorflow.op.core.SpaceToBatch;
import org.tensorflow.op.core.SpaceToBatchND;
import org.tensorflow.op.core.SpaceToDepth;
import org.tensorflow.op.core.SparseAccumulatorApplyGradient;
import org.tensorflow.op.core.SparseAccumulatorTakeGradient;
import org.tensorflow.op.core.SparseAdd;
import org.tensorflow.op.core.SparseAddGrad;
import org.tensorflow.op.core.SparseApplyAdadelta;
import org.tensorflow.op.core.SparseApplyAdagrad;
import org.tensorflow.op.core.SparseApplyAdagradDA;
import org.tensorflow.op.core.SparseApplyCenteredRMSProp;
import org.tensorflow.op.core.SparseApplyFtrl;
import org.tensorflow.op.core.SparseApplyFtrlV2;
import org.tensorflow.op.core.SparseApplyMomentum;
import org.tensorflow.op.core.SparseApplyProximalAdagrad;
import org.tensorflow.op.core.SparseApplyProximalGradientDescent;
import org.tensorflow.op.core.SparseApplyRMSProp;
import org.tensorflow.op.core.SparseConcat;
import org.tensorflow.op.core.SparseConditionalAccumulator;
import org.tensorflow.op.core.SparseCross;
import org.tensorflow.op.core.SparseDenseCwiseAdd;
import org.tensorflow.op.core.SparseDenseCwiseDiv;
import org.tensorflow.op.core.SparseDenseCwiseMul;
import org.tensorflow.op.core.SparseFillEmptyRows;
import org.tensorflow.op.core.SparseFillEmptyRowsGrad;
import org.tensorflow.op.core.SparseMatMul;
import org.tensorflow.op.core.SparseReduceMax;
import org.tensorflow.op.core.SparseReduceMaxSparse;
import org.tensorflow.op.core.SparseReduceSum;
import org.tensorflow.op.core.SparseReduceSumSparse;
import org.tensorflow.op.core.SparseReorder;
import org.tensorflow.op.core.SparseReshape;
import org.tensorflow.op.core.SparseSegmentMean;
import org.tensorflow.op.core.SparseSegmentMeanGrad;
import org.tensorflow.op.core.SparseSegmentMeanWithNumSegments;
import org.tensorflow.op.core.SparseSegmentSqrtN;
import org.tensorflow.op.core.SparseSegmentSqrtNGrad;
import org.tensorflow.op.core.SparseSegmentSqrtNWithNumSegments;
import org.tensorflow.op.core.SparseSegmentSum;
import org.tensorflow.op.core.SparseSegmentSumWithNumSegments;
import org.tensorflow.op.core.SparseSlice;
import org.tensorflow.op.core.SparseSliceGrad;
import org.tensorflow.op.core.SparseSoftmax;
import org.tensorflow.op.core.SparseSoftmaxCrossEntropyWithLogits;
import org.tensorflow.op.core.SparseSparseMaximum;
import org.tensorflow.op.core.SparseSparseMinimum;
import org.tensorflow.op.core.SparseSplit;
import org.tensorflow.op.core.SparseTensorDenseAdd;
import org.tensorflow.op.core.SparseTensorDenseMatMul;
import org.tensorflow.op.core.SparseTensorSliceDataset;
import org.tensorflow.op.core.SparseToDense;
import org.tensorflow.op.core.SparseToSparseSetOperation;
import org.tensorflow.op.core.Split;
import org.tensorflow.op.core.SplitV;
import org.tensorflow.op.core.SqlDataset;
import org.tensorflow.op.core.Sqrt;
import org.tensorflow.op.core.Square;
import org.tensorflow.op.core.SquaredDifference;
import org.tensorflow.op.core.Squeeze;
import org.tensorflow.op.core.Stack;
import org.tensorflow.op.core.Stage;
import org.tensorflow.op.core.StageClear;
import org.tensorflow.op.core.StagePeek;
import org.tensorflow.op.core.StageSize;
import org.tensorflow.op.core.StatelessMultinomial;
import org.tensorflow.op.core.StatelessRandomNormal;
import org.tensorflow.op.core.StatelessRandomUniform;
import org.tensorflow.op.core.StatelessTruncatedNormal;
import org.tensorflow.op.core.StatsAggregatorHandle;
import org.tensorflow.op.core.StatsAggregatorSummary;
import org.tensorflow.op.core.StopGradient;
import org.tensorflow.op.core.StridedSlice;
import org.tensorflow.op.core.StridedSliceAssign;
import org.tensorflow.op.core.StridedSliceGrad;
import org.tensorflow.op.core.StringJoin;
import org.tensorflow.op.core.StringSplit;
import org.tensorflow.op.core.StringSplitV2;
import org.tensorflow.op.core.StringStrip;
import org.tensorflow.op.core.StringToHashBucket;
import org.tensorflow.op.core.StringToHashBucketFast;
import org.tensorflow.op.core.StringToHashBucketStrong;
import org.tensorflow.op.core.StringToNumber;
import org.tensorflow.op.core.Sub;
import org.tensorflow.op.core.Substr;
import org.tensorflow.op.core.Subtract;
import org.tensorflow.op.core.Sum;
import org.tensorflow.op.core.Svd;
import org.tensorflow.op.core.TFRecordDataset;
import org.tensorflow.op.core.TFRecordReader;
import org.tensorflow.op.core.TakeDataset;
import org.tensorflow.op.core.TakeManySparseFromTensorsMap;
import org.tensorflow.op.core.Tan;
import org.tensorflow.op.core.Tanh;
import org.tensorflow.op.core.TemporaryVariable;
import org.tensorflow.op.core.TensorArray;
import org.tensorflow.op.core.TensorArrayClose;
import org.tensorflow.op.core.TensorArrayConcat;
import org.tensorflow.op.core.TensorArrayGather;
import org.tensorflow.op.core.TensorArrayGrad;
import org.tensorflow.op.core.TensorArrayGradWithShape;
import org.tensorflow.op.core.TensorArrayPack;
import org.tensorflow.op.core.TensorArrayRead;
import org.tensorflow.op.core.TensorArrayScatter;
import org.tensorflow.op.core.TensorArraySize;
import org.tensorflow.op.core.TensorArraySplit;
import org.tensorflow.op.core.TensorArrayUnpack;
import org.tensorflow.op.core.TensorArrayWrite;
import org.tensorflow.op.core.TensorDataset;
import org.tensorflow.op.core.TensorListConcatLists;
import org.tensorflow.op.core.TensorListElementShape;
import org.tensorflow.op.core.TensorListFromTensor;
import org.tensorflow.op.core.TensorListGetItem;
import org.tensorflow.op.core.TensorListLength;
import org.tensorflow.op.core.TensorListPopBack;
import org.tensorflow.op.core.TensorListPushBack;
import org.tensorflow.op.core.TensorListPushBackBatch;
import org.tensorflow.op.core.TensorListReserve;
import org.tensorflow.op.core.TensorListSetItem;
import org.tensorflow.op.core.TensorListStack;
import org.tensorflow.op.core.TensorSliceDataset;
import org.tensorflow.op.core.TensorSummary;
import org.tensorflow.op.core.TensorSummaryV2;
import org.tensorflow.op.core.TextLineDataset;
import org.tensorflow.op.core.TextLineReader;
import org.tensorflow.op.core.Tile;
import org.tensorflow.op.core.TileGrad;
import org.tensorflow.op.core.Timestamp;
import org.tensorflow.op.core.TopK;
import org.tensorflow.op.core.Transpose;
import org.tensorflow.op.core.TruncateDiv;
import org.tensorflow.op.core.TruncateMod;
import org.tensorflow.op.core.TruncatedNormal;
import org.tensorflow.op.core.TryRpc;
import org.tensorflow.op.core.Unbatch;
import org.tensorflow.op.core.UnbatchDataset;
import org.tensorflow.op.core.UnbatchGrad;
import org.tensorflow.op.core.UniformCandidateSampler;
import org.tensorflow.op.core.Unique;
import org.tensorflow.op.core.UniqueV2;
import org.tensorflow.op.core.UniqueWithCounts;
import org.tensorflow.op.core.UniqueWithCountsV2;
import org.tensorflow.op.core.UnravelIndex;
import org.tensorflow.op.core.UnsortedSegmentMax;
import org.tensorflow.op.core.UnsortedSegmentMin;
import org.tensorflow.op.core.UnsortedSegmentProd;
import org.tensorflow.op.core.UnsortedSegmentSum;
import org.tensorflow.op.core.Unstack;
import org.tensorflow.op.core.Unstage;
import org.tensorflow.op.core.VarHandleOp;
import org.tensorflow.op.core.VarIsInitializedOp;
import org.tensorflow.op.core.Variable;
import org.tensorflow.op.core.VariableShape;
import org.tensorflow.op.core.Where;
import org.tensorflow.op.core.Where3;
import org.tensorflow.op.core.WholeFileReader;
import org.tensorflow.op.core.WriteFile;
import org.tensorflow.op.core.ZerosLike;
import org.tensorflow.op.core.Zeta;
import org.tensorflow.op.core.ZipDataset;
import org.tensorflow.types.UInt8;

/**
 * An API for building a {@link Graph} with operation wrappers
 * 
    
 * Any operation wrapper found in the classpath properly annotated as an{@link org.tensorflow.op.annotation.Operator @Operator} is exposed
 * by this API or one of its subgroup.
 * 
    Example usage:
 * 
    {@code
 * try (Graph g = new Graph()) {
 *   Ops ops = new Ops(g);
 *   // Operations are typed classes with convenience
 *   // builders in Ops.
 *   Constant three = ops.constant(3);
 *   // Single-result operations implement the Operand
 *   // interface, so this works too.
 *   Operand four = ops.constant(4);
 *   // Most builders are found within a group, and accept
 *   // Operand types as operands
 *   Operand nine = ops.math().add(four, ops.constant(5));
 *   // Multi-result operations however offer methods to
 *   // select a particular result for use.
 *   Operand result = 
 *       ops.math().add(ops.array().unique(s, a).y(), b);
 *   // Optional attributes
 *   ops.math().matMul(a, b, MatMul.transposeA(true));
 *   // Naming operators
 *   ops.withName(“foo”).constant(5); // name “foo”
 *   // Names can exist in a hierarchy
 *   Ops sub = ops.withSubScope(“sub”);
 *   sub.withName(“bar”).constant(4); // “sub/bar”
 * }
 * }
    
 */
public final class Ops {
  private final Scope scope;

  private Ops(Scope scope) {
    this.scope = scope;
  }

  /**
   * Adds an {@link QueueSize} operation to the graph
   *
   * @param handle The handle to a queue.
   * @return a new instance of QueueSize
   * @see {@link org.tensorflow.op.core.QueueSize}
   */
  public QueueSize queueSize(Operand handle) {
    return QueueSize.create(scope, handle);
  }

  /**
   * Adds an {@link Tile} operation to the graph
   *
   * @param input 1-D or higher.
   * @param multiples 1-D. Length must be the same as the number of dimensions in `input`
   * @return a new instance of Tile
   * @see {@link org.tensorflow.op.core.Tile}
   */
  public  Tile tile(Operand input, Operand multiples) {
    return Tile.create(scope, input, multiples);
  }

  /**
   * Adds an {@link SparseTensorDenseMatMul} operation to the graph
   *
   * @param aIndices 2-D.  The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix.
   * @param aValues 1-D.  The `values` of the `SparseTensor`, size `[nnz]` Vector.
   * @param aShape 1-D.  The `shape` of the `SparseTensor`, size `[2]` Vector.
   * @param b 2-D.  A dense Matrix.
   * @param options carries optional attributes values
   * @return a new instance of SparseTensorDenseMatMul
   * @see {@link org.tensorflow.op.core.SparseTensorDenseMatMul}
   */
  public  SparseTensorDenseMatMul sparseTensorDenseMatMul(Operand aIndices,
      Operand aValues, Operand aShape, Operand b,
      SparseTensorDenseMatMul.Options... options) {
    return SparseTensorDenseMatMul.create(scope, aIndices, aValues, aShape, b, options);
  }

  /**
   * Adds an {@link BatchMatrixDiag} operation to the graph
   *
   * @param diagonal 
   * @return a new instance of BatchMatrixDiag
   * @see {@link org.tensorflow.op.core.BatchMatrixDiag}
   */
  public  BatchMatrixDiag batchMatrixDiag(Operand diagonal) {
    return BatchMatrixDiag.create(scope, diagonal);
  }

  /**
   * Adds an {@link EncodeProto} operation to the graph
   *
   * @param sizes Tensor of int32 with shape `[batch_shape, len(field_names)]`.
   * @param values List of tensors containing values for the corresponding field.
   * @param fieldNames List of strings containing proto field names.
   * @param messageType Name of the proto message type to decode.
   * @param options carries optional attributes values
   * @return a new instance of EncodeProto
   * @see {@link org.tensorflow.op.core.EncodeProto}
   */
  public EncodeProto encodeProto(Operand sizes, Iterable> values,
      List fieldNames, String messageType, EncodeProto.Options... options) {
    return EncodeProto.create(scope, sizes, values, fieldNames, messageType, options);
  }

  /**
   * Adds an {@link Conv3DBackpropInputV2} operation to the graph
   *
   * @param inputSizes An integer vector representing the tensor shape of `input`,
   * @param filter Shape `[depth, rows, cols, in_channels, out_channels]`.
   * @param outBackprop Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
   * @param strides 1-D tensor of length 5. The stride of the sliding window for each
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of Conv3DBackpropInputV2
   * @see {@link org.tensorflow.op.core.Conv3DBackpropInputV2}
   */
  public  Conv3DBackpropInputV2 conv3DBackpropInputV2(Operand inputSizes,
      Operand filter, Operand outBackprop, List strides, String padding,
      Conv3DBackpropInputV2.Options... options) {
    return Conv3DBackpropInputV2.create(scope, inputSizes, filter, outBackprop, strides, padding, options);
  }

  /**
   * Adds an {@link IteratorGetNext} operation to the graph
   *
   * @param iterator 
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of IteratorGetNext
   * @see {@link org.tensorflow.op.core.IteratorGetNext}
   */
  public IteratorGetNext iteratorGetNext(Operand iterator, List> outputTypes,
      List outputShapes) {
    return IteratorGetNext.create(scope, iterator, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link SparseSegmentMean} operation to the graph
   *
   * @param data 
   * @param indices A 1-D tensor. Has same rank as `segment_ids`.
   * @param segmentIds A 1-D tensor. Values should be sorted and can be repeated.
   * @return a new instance of SparseSegmentMean
   * @see {@link org.tensorflow.op.core.SparseSegmentMean}
   */
  public  SparseSegmentMean sparseSegmentMean(Operand data,
      Operand indices, Operand segmentIds) {
    return SparseSegmentMean.create(scope, data, indices, segmentIds);
  }

  /**
   * Adds an {@link LMDBReader} operation to the graph
   *
   * @param options carries optional attributes values
   * @return a new instance of LMDBReader
   * @see {@link org.tensorflow.op.core.LMDBReader}
   */
  public LMDBReader lMDBReader(LMDBReader.Options... options) {
    return LMDBReader.create(scope, options);
  }

  /**
   * Adds an {@link ArgMin} operation to the graph
   *
   * @param input 
   * @param dimension int32 or int64, must be in the range `[-rank(input), rank(input))`.
   * @param outputType 
   * @return a new instance of ArgMin
   * @see {@link org.tensorflow.op.core.ArgMin}
   */
  public  ArgMin argMin(Operand input,
      Operand dimension, Class outputType) {
    return ArgMin.create(scope, input, dimension, outputType);
  }

  /**
   * Adds an {@link ApplyFtrlV2} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param linear Should be from a Variable().
   * @param grad The gradient.
   * @param lr Scaling factor. Must be a scalar.
   * @param l1 L1 regulariation. Must be a scalar.
   * @param l2 L2 shrinkage regulariation. Must be a scalar.
   * @param l2Shrinkage 
   * @param lrPower Scaling factor. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of ApplyFtrlV2
   * @see {@link org.tensorflow.op.core.ApplyFtrlV2}
   */
  public  ApplyFtrlV2 applyFtrlV2(Operand var, Operand accum, Operand linear,
      Operand grad, Operand lr, Operand l1, Operand l2, Operand l2Shrinkage,
      Operand lrPower, ApplyFtrlV2.Options... options) {
    return ApplyFtrlV2.create(scope, var, accum, linear, grad, lr, l1, l2, l2Shrinkage, lrPower, options);
  }

  /**
   * Adds an {@link IRFFT3D} operation to the graph
   *
   * @param input A complex64 tensor.
   * @param fftLength An int32 tensor of shape [3]. The FFT length for each dimension.
   * @return a new instance of IRFFT3D
   * @see {@link org.tensorflow.op.core.IRFFT3D}
   */
  public IRFFT3D iRFFT3D(Operand input, Operand fftLength) {
    return IRFFT3D.create(scope, input, fftLength);
  }

  /**
   * Adds an {@link TensorListGetItem} operation to the graph
   *
   * @param inputHandle 
   * @param index 
   * @param elementDtype 
   * @return a new instance of TensorListGetItem
   * @see {@link org.tensorflow.op.core.TensorListGetItem}
   */
  public  TensorListGetItem tensorListGetItem(Operand inputHandle, Operand index,
      Class elementDtype) {
    return TensorListGetItem.create(scope, inputHandle, index, elementDtype);
  }

  /**
   * Adds an {@link AvgPool} operation to the graph
   *
   * @param value 4-D with shape `[batch, height, width, channels]`.
   * @param ksize The size of the sliding window for each dimension of `value`.
   * @param strides The stride of the sliding window for each dimension of `value`.
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of AvgPool
   * @see {@link org.tensorflow.op.core.AvgPool}
   */
  public  AvgPool avgPool(Operand value, List ksize,
      List strides, String padding, AvgPool.Options... options) {
    return AvgPool.create(scope, value, ksize, strides, padding, options);
  }

  /**
   * Adds an {@link QueueClose} operation to the graph
   *
   * @param handle The handle to a queue.
   * @param options carries optional attributes values
   * @return a new instance of QueueClose
   * @see {@link org.tensorflow.op.core.QueueClose}
   */
  public QueueClose queueClose(Operand handle, QueueClose.Options... options) {
    return QueueClose.create(scope, handle, options);
  }

  /**
   * Adds an {@link SquaredDifference} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of SquaredDifference
   * @see {@link org.tensorflow.op.core.SquaredDifference}
   */
  public  SquaredDifference squaredDifference(Operand x, Operand y) {
    return SquaredDifference.create(scope, x, y);
  }

  /**
   * Adds an {@link SparseTensorSliceDataset} operation to the graph
   *
   * @param indices 
   * @param values 
   * @param denseShape 
   * @return a new instance of SparseTensorSliceDataset
   * @see {@link org.tensorflow.op.core.SparseTensorSliceDataset}
   */
  public  SparseTensorSliceDataset sparseTensorSliceDataset(Operand indices,
      Operand values, Operand denseShape) {
    return SparseTensorSliceDataset.create(scope, indices, values, denseShape);
  }

  /**
   * Adds an {@link Minimum} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of Minimum
   * @see {@link org.tensorflow.op.core.Minimum}
   */
  public  Minimum minimum(Operand x, Operand y) {
    return Minimum.create(scope, x, y);
  }

  /**
   * Adds an {@link Barrier} operation to the graph
   *
   * @param componentTypes The type of each component in a value.
   * @param options carries optional attributes values
   * @return a new instance of Barrier
   * @see {@link org.tensorflow.op.core.Barrier}
   */
  public Barrier barrier(List> componentTypes, Barrier.Options... options) {
    return Barrier.create(scope, componentTypes, options);
  }

  /**
   * Adds an {@link ApplyProximalAdagrad} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 regularization. Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ApplyProximalAdagrad
   * @see {@link org.tensorflow.op.core.ApplyProximalAdagrad}
   */
  public  ApplyProximalAdagrad applyProximalAdagrad(Operand var, Operand accum,
      Operand lr, Operand l1, Operand l2, Operand grad,
      ApplyProximalAdagrad.Options... options) {
    return ApplyProximalAdagrad.create(scope, var, accum, lr, l1, l2, grad, options);
  }

  /**
   * Adds an {@link DiagPart} operation to the graph
   *
   * @param input Rank k tensor where k is even and not zero.
   * @return a new instance of DiagPart
   * @see {@link org.tensorflow.op.core.DiagPart}
   */
  public  DiagPart diagPart(Operand input) {
    return DiagPart.create(scope, input);
  }

  /**
   * Adds an {@link ParameterizedTruncatedNormal} operation to the graph
   *
   * @param shape The shape of the output tensor. Batches are indexed by the 0th dimension.
   * @param means The mean parameter of each batch.
   * @param stdevs The standard deviation parameter of each batch. Must be greater than 0.
   * @param minvals The minimum cutoff. May be -infinity.
   * @param maxvals The maximum cutoff. May be +infinity, and must be more than the minval
   * @param options carries optional attributes values
   * @return a new instance of ParameterizedTruncatedNormal
   * @see {@link org.tensorflow.op.core.ParameterizedTruncatedNormal}
   */
  public  ParameterizedTruncatedNormal parameterizedTruncatedNormal(Operand shape,
      Operand means, Operand stdevs, Operand minvals, Operand maxvals,
      ParameterizedTruncatedNormal.Options... options) {
    return ParameterizedTruncatedNormal.create(scope, shape, means, stdevs, minvals, maxvals, options);
  }

  /**
   * Adds an {@link Dilation2DBackpropFilter} operation to the graph
   *
   * @param input 4-D with shape `[batch, in_height, in_width, depth]`.
   * @param filter 3-D with shape `[filter_height, filter_width, depth]`.
   * @param outBackprop 4-D with shape `[batch, out_height, out_width, depth]`.
   * @param strides 1-D of length 4. The stride of the sliding window for each dimension of
   * @param rates 1-D of length 4. The input stride for atrous morphological dilation.
   * @param padding The type of padding algorithm to use.
   * @return a new instance of Dilation2DBackpropFilter
   * @see {@link org.tensorflow.op.core.Dilation2DBackpropFilter}
   */
  public  Dilation2DBackpropFilter dilation2DBackpropFilter(Operand input,
      Operand filter, Operand outBackprop, List strides, List rates,
      String padding) {
    return Dilation2DBackpropFilter.create(scope, input, filter, outBackprop, strides, rates, padding);
  }

  /**
   * Adds an {@link StringStrip} operation to the graph
   *
   * @param input A string `Tensor` of any shape.
   * @return a new instance of StringStrip
   * @see {@link org.tensorflow.op.core.StringStrip}
   */
  public StringStrip stringStrip(Operand input) {
    return StringStrip.create(scope, input);
  }

  /**
   * Adds an {@link AddV2} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of AddV2
   * @see {@link org.tensorflow.op.core.AddV2}
   */
  public  AddV2 addV2(Operand x, Operand y) {
    return AddV2.create(scope, x, y);
  }

  /**
   * Adds an {@link ParallelDynamicStitch} operation to the graph
   *
   * @param indices 
   * @param data 
   * @return a new instance of ParallelDynamicStitch
   * @see {@link org.tensorflow.op.core.ParallelDynamicStitch}
   */
  public  ParallelDynamicStitch parallelDynamicStitch(Iterable> indices,
      Operand data) {
    return ParallelDynamicStitch.create(scope, indices, data);
  }

  /**
   * Adds an {@link QuantizedMaxPool} operation to the graph
   *
   * @param input The 4D (batch x rows x cols x depth) Tensor to MaxReduce over.
   * @param minInput The float value that the lowest quantized input value represents.
   * @param maxInput The float value that the highest quantized input value represents.
   * @param ksize The size of the window for each dimension of the input tensor.
   * @param strides The stride of the sliding window for each dimension of the input
   * @param padding The type of padding algorithm to use.
   * @return a new instance of QuantizedMaxPool
   * @see {@link org.tensorflow.op.core.QuantizedMaxPool}
   */
  public  QuantizedMaxPool quantizedMaxPool(Operand input, Operand minInput,
      Operand maxInput, List ksize, List strides, String padding) {
    return QuantizedMaxPool.create(scope, input, minInput, maxInput, ksize, strides, padding);
  }

  /**
   * Adds an {@link Constant} operation to the graph
   *
   * @param shape the tensor shape.
   * @param data a buffer containing the tensor data.
   * @throws IllegalArgumentException If the tensor shape is not compatible with the buffer
   * @see {@link org.tensorflow.op.core.Constant}
   */
  public Constant constant(long[] shape, DoubleBuffer data) {
    return Constant.create(scope, shape, data);
  }

  /**
   * Adds an {@link MergeV2Checkpoints} operation to the graph
   *
   * @param checkpointPrefixes prefixes of V2 checkpoints to merge.
   * @param destinationPrefix scalar.  The desired final prefix.  Allowed to be the same
   * @param options carries optional attributes values
   * @return a new instance of MergeV2Checkpoints
   * @see {@link org.tensorflow.op.core.MergeV2Checkpoints}
   */
  public MergeV2Checkpoints mergeV2Checkpoints(Operand checkpointPrefixes,
      Operand destinationPrefix, MergeV2Checkpoints.Options... options) {
    return MergeV2Checkpoints.create(scope, checkpointPrefixes, destinationPrefix, options);
  }

  /**
   * Adds an {@link Conv3DBackpropFilter} operation to the graph
   *
   * @param input Shape `[batch, depth, rows, cols, in_channels]`.
   * @param filter Shape `[depth, rows, cols, in_channels, out_channels]`.
   * @param outBackprop Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
   * @param strides 1-D tensor of length 5. The stride of the sliding window for each
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of Conv3DBackpropFilter
   * @see {@link org.tensorflow.op.core.Conv3DBackpropFilter}
   */
  public  Conv3DBackpropFilter conv3DBackpropFilter(Operand input,
      Operand filter, Operand outBackprop, List strides, String padding,
      Conv3DBackpropFilter.Options... options) {
    return Conv3DBackpropFilter.create(scope, input, filter, outBackprop, strides, padding, options);
  }

  /**
   * Adds an {@link DatasetToSingleElement} operation to the graph
   *
   * @param dataset A handle to a dataset that contains a single element.
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of DatasetToSingleElement
   * @see {@link org.tensorflow.op.core.DatasetToSingleElement}
   */
  public DatasetToSingleElement datasetToSingleElement(Operand dataset,
      List> outputTypes, List outputShapes) {
    return DatasetToSingleElement.create(scope, dataset, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link TensorDataset} operation to the graph
   *
   * @param components 
   * @param outputShapes 
   * @return a new instance of TensorDataset
   * @see {@link org.tensorflow.op.core.TensorDataset}
   */
  public TensorDataset tensorDataset(Iterable> components, List outputShapes) {
    return TensorDataset.create(scope, components, outputShapes);
  }

  /**
   * Adds an {@link QuantizedMatMul} operation to the graph
   *
   * @param a Must be a two-dimensional tensor.
   * @param b Must be a two-dimensional tensor.
   * @param minA The float value that the lowest quantized `a` value represents.
   * @param maxA The float value that the highest quantized `a` value represents.
   * @param minB The float value that the lowest quantized `b` value represents.
   * @param maxB The float value that the highest quantized `b` value represents.
   * @param Toutput 
   * @param Tactivation The type of output produced by activation function
   * @param options carries optional attributes values
   * @return a new instance of QuantizedMatMul
   * @see {@link org.tensorflow.op.core.QuantizedMatMul}
   */
  public  QuantizedMatMul quantizedMatMul(Operand a, Operand b,
      Operand minA, Operand maxA, Operand minB, Operand maxB,
      Class Toutput, Class Tactivation, QuantizedMatMul.Options... options) {
    return QuantizedMatMul.create(scope, a, b, minA, maxA, minB, maxB, Toutput, Tactivation, options);
  }

  /**
   * Adds an {@link InTopK} operation to the graph
   *
   * @param predictions A `batch_size` x `classes` tensor.
   * @param targets A `batch_size` vector of class ids.
   * @param k Number of top elements to look at for computing precision.
   * @return a new instance of InTopK
   * @see {@link org.tensorflow.op.core.InTopK}
   */
  public  InTopK inTopK(Operand predictions, Operand targets, Long k) {
    return InTopK.create(scope, predictions, targets, k);
  }

  /**
   * Adds an {@link TensorListSetItem} operation to the graph
   *
   * @param inputHandle 
   * @param index 
   * @param item 
   * @return a new instance of TensorListSetItem
   * @see {@link org.tensorflow.op.core.TensorListSetItem}
   */
  public  TensorListSetItem tensorListSetItem(Operand inputHandle, Operand index,
      Operand item) {
    return TensorListSetItem.create(scope, inputHandle, index, item);
  }

  /**
   * Adds an {@link SegmentMean} operation to the graph
   *
   * @param data 
   * @param segmentIds A 1-D tensor whose rank is equal to the rank of `data`'s
   * @return a new instance of SegmentMean
   * @see {@link org.tensorflow.op.core.SegmentMean}
   */
  public  SegmentMean segmentMean(Operand data, Operand segmentIds) {
    return SegmentMean.create(scope, data, segmentIds);
  }

  /**
   * Adds an {@link Slice} operation to the graph
   *
   * @param input 
   * @param begin begin[i] specifies the offset into the 'i'th dimension of
   * @param size size[i] specifies the number of elements of the 'i'th dimension
   * @return a new instance of Slice
   * @see {@link org.tensorflow.op.core.Slice}
   */
  public  Slice slice(Operand input, Operand begin, Operand size) {
    return Slice.create(scope, input, begin, size);
  }

  /**
   * Adds an {@link ApplyAdagradDA} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param gradientAccumulator Should be from a Variable().
   * @param gradientSquaredAccumulator Should be from a Variable().
   * @param grad The gradient.
   * @param lr Scaling factor. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 regularization. Must be a scalar.
   * @param globalStep Training step number. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of ApplyAdagradDA
   * @see {@link org.tensorflow.op.core.ApplyAdagradDA}
   */
  public  ApplyAdagradDA applyAdagradDA(Operand var, Operand gradientAccumulator,
      Operand gradientSquaredAccumulator, Operand grad, Operand lr, Operand l1,
      Operand l2, Operand globalStep, ApplyAdagradDA.Options... options) {
    return ApplyAdagradDA.create(scope, var, gradientAccumulator, gradientSquaredAccumulator, grad, lr, l1, l2, globalStep, options);
  }

  /**
   * Adds an {@link QuantizeAndDequantizeV3} operation to the graph
   *
   * @param input 
   * @param inputMin 
   * @param inputMax 
   * @param numBits 
   * @param options carries optional attributes values
   * @return a new instance of QuantizeAndDequantizeV3
   * @see {@link org.tensorflow.op.core.QuantizeAndDequantizeV3}
   */
  public  QuantizeAndDequantizeV3 quantizeAndDequantizeV3(Operand input,
      Operand inputMin, Operand inputMax, Operand numBits,
      QuantizeAndDequantizeV3.Options... options) {
    return QuantizeAndDequantizeV3.create(scope, input, inputMin, inputMax, numBits, options);
  }

  /**
   * Adds an {@link DenseToDenseSetOperation} operation to the graph
   *
   * @param set1 `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`.
   * @param set2 `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set1`.
   * @param setOperation 
   * @param options carries optional attributes values
   * @return a new instance of DenseToDenseSetOperation
   * @see {@link org.tensorflow.op.core.DenseToDenseSetOperation}
   */
  public  DenseToDenseSetOperation denseToDenseSetOperation(Operand set1, Operand set2,
      String setOperation, DenseToDenseSetOperation.Options... options) {
    return DenseToDenseSetOperation.create(scope, set1, set2, setOperation, options);
  }

  /**
   * Adds an {@link BatchMatrixTriangularSolve} operation to the graph
   *
   * @param matrix 
   * @param rhs 
   * @param options carries optional attributes values
   * @return a new instance of BatchMatrixTriangularSolve
   * @see {@link org.tensorflow.op.core.BatchMatrixTriangularSolve}
   */
  public  BatchMatrixTriangularSolve batchMatrixTriangularSolve(Operand matrix,
      Operand rhs, BatchMatrixTriangularSolve.Options... options) {
    return BatchMatrixTriangularSolve.create(scope, matrix, rhs, options);
  }

  /**
   * Adds an {@link FakeQuantWithMinMaxArgs} operation to the graph
   *
   * @param inputs 
   * @param options carries optional attributes values
   * @return a new instance of FakeQuantWithMinMaxArgs
   * @see {@link org.tensorflow.op.core.FakeQuantWithMinMaxArgs}
   */
  public FakeQuantWithMinMaxArgs fakeQuantWithMinMaxArgs(Operand inputs,
      FakeQuantWithMinMaxArgs.Options... options) {
    return FakeQuantWithMinMaxArgs.create(scope, inputs, options);
  }

  /**
   * Adds an {@link ResizeBilinear} operation to the graph
   *
   * @param images 4-D with shape `[batch, height, width, channels]`.
   * @param size = A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
   * @param options carries optional attributes values
   * @return a new instance of ResizeBilinear
   * @see {@link org.tensorflow.op.core.ResizeBilinear}
   */
  public  ResizeBilinear resizeBilinear(Operand images, Operand size,
      ResizeBilinear.Options... options) {
    return ResizeBilinear.create(scope, images, size, options);
  }

  /**
   * Adds an {@link MapPeek} operation to the graph
   *
   * @param key 
   * @param indices 
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of MapPeek
   * @see {@link org.tensorflow.op.core.MapPeek}
   */
  public MapPeek mapPeek(Operand key, Operand indices, List> dtypes,
      MapPeek.Options... options) {
    return MapPeek.create(scope, key, indices, dtypes, options);
  }

  /**
   * Adds an {@link TensorArrayGather} operation to the graph
   *
   * @param handle The handle to a TensorArray.
   * @param indices The locations in the TensorArray from which to read tensor elements.
   * @param flowIn A float scalar that enforces proper chaining of operations.
   * @param dtype The type of the elem that is returned.
   * @param options carries optional attributes values
   * @return a new instance of TensorArrayGather
   * @see {@link org.tensorflow.op.core.TensorArrayGather}
   */
  public  TensorArrayGather tensorArrayGather(Operand handle, Operand indices,
      Operand flowIn, Class dtype, TensorArrayGather.Options... options) {
    return TensorArrayGather.create(scope, handle, indices, flowIn, dtype, options);
  }

  /**
   * Adds an {@link BatchMatrixInverse} operation to the graph
   *
   * @param input 
   * @param options carries optional attributes values
   * @return a new instance of BatchMatrixInverse
   * @see {@link org.tensorflow.op.core.BatchMatrixInverse}
   */
  public  BatchMatrixInverse batchMatrixInverse(Operand input,
      BatchMatrixInverse.Options... options) {
    return BatchMatrixInverse.create(scope, input, options);
  }

  /**
   * Adds an {@link Placeholder} operation to the graph
   *
   * @param dtype The type of elements in the tensor.
   * @param options carries optional attributes values
   * @return a new instance of Placeholder
   * @see {@link org.tensorflow.op.core.Placeholder}
   */
  public  Placeholder placeholder(Class dtype, Placeholder.Options... options) {
    return Placeholder.create(scope, dtype, options);
  }

  /**
   * Adds an {@link SdcaShrinkL1} operation to the graph
   *
   * @param weights a list of vectors where each value is the weight associated with a
   * @param l1 Symmetric l1 regularization strength.
   * @param l2 Symmetric l2 regularization strength. Should be a positive float.
   * @return a new instance of SdcaShrinkL1
   * @see {@link org.tensorflow.op.core.SdcaShrinkL1}
   */
  public SdcaShrinkL1 sdcaShrinkL1(Iterable> weights, Float l1, Float l2) {
    return SdcaShrinkL1.create(scope, weights, l1, l2);
  }

  /**
   * Adds an {@link DeleteSessionTensor} operation to the graph
   *
   * @param handle The handle for a tensor stored in the session state.
   * @return a new instance of DeleteSessionTensor
   * @see {@link org.tensorflow.op.core.DeleteSessionTensor}
   */
  public DeleteSessionTensor deleteSessionTensor(Operand handle) {
    return DeleteSessionTensor.create(scope, handle);
  }

  /**
   * Adds an {@link SerializeManySparse} operation to the graph
   *
   * @param sparseIndices 2-D.  The `indices` of the minibatch `SparseTensor`.
   * @param sparseValues 1-D.  The `values` of the minibatch `SparseTensor`.
   * @param sparseShape 1-D.  The `shape` of the minibatch `SparseTensor`.
   * @param outType The `dtype` to use for serialization; the supported types are `string`
   * @return a new instance of SerializeManySparse
   * @see {@link org.tensorflow.op.core.SerializeManySparse}
   */
  public  SerializeManySparse serializeManySparse(Operand sparseIndices,
      Operand sparseValues, Operand sparseShape, Class outType) {
    return SerializeManySparse.create(scope, sparseIndices, sparseValues, sparseShape, outType);
  }

  /**
   * Adds an {@link TFRecordReader} operation to the graph
   *
   * @param options carries optional attributes values
   * @return a new instance of TFRecordReader
   * @see {@link org.tensorflow.op.core.TFRecordReader}
   */
  public TFRecordReader tFRecordReader(TFRecordReader.Options... options) {
    return TFRecordReader.create(scope, options);
  }

  /**
   * Adds an {@link RestoreV2} operation to the graph
   *
   * @param prefix Must have a single element.  The prefix of a V2 checkpoint.
   * @param tensorNames shape {N}.  The names of the tensors to be restored.
   * @param shapeAndSlices shape {N}.  The slice specs of the tensors to be restored.
   * @param dtypes shape {N}.  The list of expected dtype for the tensors.  Must match
   * @return a new instance of RestoreV2
   * @see {@link org.tensorflow.op.core.RestoreV2}
   */
  public RestoreV2 restoreV2(Operand prefix, Operand tensorNames,
      Operand shapeAndSlices, List> dtypes) {
    return RestoreV2.create(scope, prefix, tensorNames, shapeAndSlices, dtypes);
  }

  /**
   * Adds an {@link InitializeTable} operation to the graph
   *
   * @param tableHandle Handle to a table which will be initialized.
   * @param keys Keys of type Tkey.
   * @param values Values of type Tval.
   * @return a new instance of InitializeTable
   * @see {@link org.tensorflow.op.core.InitializeTable}
   */
  public  InitializeTable initializeTable(Operand tableHandle, Operand keys,
      Operand values) {
    return InitializeTable.create(scope, tableHandle, keys, values);
  }

  /**
   * Adds an {@link RequantizationRange} operation to the graph
   *
   * @param input 
   * @param inputMin The float value that the minimum quantized input value represents.
   * @param inputMax The float value that the maximum quantized input value represents.
   * @return a new instance of RequantizationRange
   * @see {@link org.tensorflow.op.core.RequantizationRange}
   */
  public  RequantizationRange requantizationRange(Operand input, Operand inputMin,
      Operand inputMax) {
    return RequantizationRange.create(scope, input, inputMin, inputMax);
  }

  /**
   * Adds an {@link ComplexAbs} operation to the graph
   *
   * @param x 
   * @param Tout 
   * @return a new instance of ComplexAbs
   * @see {@link org.tensorflow.op.core.ComplexAbs}
   */
  public  ComplexAbs complexAbs(Operand x, Class Tout) {
    return ComplexAbs.create(scope, x, Tout);
  }

  /**
   * Adds an {@link Conv2D} operation to the graph
   *
   * @param input A 4-D tensor. The dimension order is interpreted according to the value
   * @param filter A 4-D tensor of shape
   * @param strides 1-D tensor of length 4.  The stride of the sliding window for each
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of Conv2D
   * @see {@link org.tensorflow.op.core.Conv2D}
   */
  public  Conv2D conv2D(Operand input, Operand filter,
      List strides, String padding, Conv2D.Options... options) {
    return Conv2D.create(scope, input, filter, strides, padding, options);
  }

  /**
   * Adds an {@link MaxPoolWithArgmax} operation to the graph
   *
   * @param input 4-D with shape `[batch, height, width, channels]`.  Input to pool over.
   * @param ksize The size of the window for each dimension of the input tensor.
   * @param strides The stride of the sliding window for each dimension of the
   * @param Targmax 
   * @param padding The type of padding algorithm to use.
   * @return a new instance of MaxPoolWithArgmax
   * @see {@link org.tensorflow.op.core.MaxPoolWithArgmax}
   */
  public  MaxPoolWithArgmax maxPoolWithArgmax(Operand input,
      List ksize, List strides, Class Targmax, String padding) {
    return MaxPoolWithArgmax.create(scope, input, ksize, strides, Targmax, padding);
  }

  /**
   * Adds an {@link TensorArrayUnpack} operation to the graph
   *
   * @param handle 
   * @param value 
   * @param flowIn 
   * @return a new instance of TensorArrayUnpack
   * @see {@link org.tensorflow.op.core.TensorArrayUnpack}
   */
  public  TensorArrayUnpack tensorArrayUnpack(Operand handle, Operand value,
      Operand flowIn) {
    return TensorArrayUnpack.create(scope, handle, value, flowIn);
  }

  /**
   * Adds an {@link Skipgram} operation to the graph
   *
   * @param filename The corpus's text file name.
   * @param batchSize The size of produced batch.
   * @param options carries optional attributes values
   * @return a new instance of Skipgram
   * @see {@link org.tensorflow.op.core.Skipgram}
   */
  public Skipgram skipgram(String filename, Long batchSize, Skipgram.Options... options) {
    return Skipgram.create(scope, filename, batchSize, options);
  }

  /**
   * Adds an {@link Sign} operation to the graph
   *
   * @param x 
   * @return a new instance of Sign
   * @see {@link org.tensorflow.op.core.Sign}
   */
  public  Sign sign(Operand x) {
    return Sign.create(scope, x);
  }

  /**
   * Adds an {@link TryRpc} operation to the graph
   *
   * @param address `0-D` or `1-D`.  The address (i.e. host_name:port) of the RPC server.
   * @param method `0-D` or `1-D`.  The method address on the RPC server.
   * @param request `0-D` or `1-D`.  Serialized proto strings: the rpc request argument.
   * @param options carries optional attributes values
   * @return a new instance of TryRpc
   * @see {@link org.tensorflow.op.core.TryRpc}
   */
  public TryRpc tryRpc(Operand address, Operand method, Operand request,
      TryRpc.Options... options) {
    return TryRpc.create(scope, address, method, request, options);
  }

  /**
   * Adds an {@link ImageSummary} operation to the graph
   *
   * @param tag Scalar. Used to build the `tag` attribute of the summary values.
   * @param tensor 4-D of shape `[batch_size, height, width, channels]` where
   * @param options carries optional attributes values
   * @return a new instance of ImageSummary
   * @see {@link org.tensorflow.op.core.ImageSummary}
   */
  public  ImageSummary imageSummary(Operand tag, Operand tensor,
      ImageSummary.Options... options) {
    return ImageSummary.create(scope, tag, tensor, options);
  }

  /**
   * Adds an {@link SparseSegmentSqrtNWithNumSegments} operation to the graph
   *
   * @param data 
   * @param indices A 1-D tensor. Has same rank as `segment_ids`.
   * @param segmentIds A 1-D tensor. Values should be sorted and can be repeated.
   * @param numSegments Should equal the number of distinct segment IDs.
   * @return a new instance of SparseSegmentSqrtNWithNumSegments
   * @see {@link org.tensorflow.op.core.SparseSegmentSqrtNWithNumSegments}
   */
  public  SparseSegmentSqrtNWithNumSegments sparseSegmentSqrtNWithNumSegments(Operand data,
      Operand indices, Operand segmentIds, Operand numSegments) {
    return SparseSegmentSqrtNWithNumSegments.create(scope, data, indices, segmentIds, numSegments);
  }

  /**
   * Adds an {@link Cos} operation to the graph
   *
   * @param x 
   * @return a new instance of Cos
   * @see {@link org.tensorflow.op.core.Cos}
   */
  public  Cos cos(Operand x) {
    return Cos.create(scope, x);
  }

  /**
   * Adds an {@link ResourceApplyFtrlV2} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param linear Should be from a Variable().
   * @param grad The gradient.
   * @param lr Scaling factor. Must be a scalar.
   * @param l1 L1 regulariation. Must be a scalar.
   * @param l2 L2 shrinkage regulariation. Must be a scalar.
   * @param l2Shrinkage 
   * @param lrPower Scaling factor. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of ResourceApplyFtrlV2
   * @see {@link org.tensorflow.op.core.ResourceApplyFtrlV2}
   */
  public  ResourceApplyFtrlV2 resourceApplyFtrlV2(Operand var, Operand accum,
      Operand linear, Operand grad, Operand lr, Operand l1, Operand l2,
      Operand l2Shrinkage, Operand lrPower, ResourceApplyFtrlV2.Options... options) {
    return ResourceApplyFtrlV2.create(scope, var, accum, linear, grad, lr, l1, l2, l2Shrinkage, lrPower, options);
  }

  /**
   * Adds an {@link RightShift} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of RightShift
   * @see {@link org.tensorflow.op.core.RightShift}
   */
  public  RightShift rightShift(Operand x, Operand y) {
    return RightShift.create(scope, x, y);
  }

  /**
   * Adds an {@link TextLineReader} operation to the graph
   *
   * @param options carries optional attributes values
   * @return a new instance of TextLineReader
   * @see {@link org.tensorflow.op.core.TextLineReader}
   */
  public TextLineReader textLineReader(TextLineReader.Options... options) {
    return TextLineReader.create(scope, options);
  }

  /**
   * Adds an {@link SparseApplyCenteredRMSProp} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param mg Should be from a Variable().
   * @param ms Should be from a Variable().
   * @param mom Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param rho Decay rate. Must be a scalar.
   * @param momentum 
   * @param epsilon Ridge term. Must be a scalar.
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var, ms and mom.
   * @param options carries optional attributes values
   * @return a new instance of SparseApplyCenteredRMSProp
   * @see {@link org.tensorflow.op.core.SparseApplyCenteredRMSProp}
   */
  public  SparseApplyCenteredRMSProp sparseApplyCenteredRMSProp(Operand var,
      Operand mg, Operand ms, Operand mom, Operand lr, Operand rho,
      Operand momentum, Operand epsilon, Operand grad, Operand indices,
      SparseApplyCenteredRMSProp.Options... options) {
    return SparseApplyCenteredRMSProp.create(scope, var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, options);
  }

  /**
   * Adds an {@link SparseApplyFtrlV2} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param linear Should be from a Variable().
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param lr Scaling factor. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 shrinkage regulariation. Must be a scalar.
   * @param l2Shrinkage 
   * @param lrPower Scaling factor. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of SparseApplyFtrlV2
   * @see {@link org.tensorflow.op.core.SparseApplyFtrlV2}
   */
  public  SparseApplyFtrlV2 sparseApplyFtrlV2(Operand var,
      Operand accum, Operand linear, Operand grad, Operand indices, Operand lr,
      Operand l1, Operand l2, Operand l2Shrinkage, Operand lrPower,
      SparseApplyFtrlV2.Options... options) {
    return SparseApplyFtrlV2.create(scope, var, accum, linear, grad, indices, lr, l1, l2, l2Shrinkage, lrPower, options);
  }

  /**
   * Adds an {@link Merge} operation to the graph
   *
   * @param inputs The input tensors, exactly one of which will become available.
   * @return a new instance of Merge
   * @see {@link org.tensorflow.op.core.Merge}
   */
  public  Merge merge(Operand inputs) {
    return Merge.create(scope, inputs);
  }

  /**
   * Adds an {@link SetDiff1D} operation to the graph
   *
   * @param x 1-D. Values to keep.
   * @param y 1-D. Values to remove.
   * @param outIdx 
   * @return a new instance of SetDiff1D
   * @see {@link org.tensorflow.op.core.SetDiff1D}
   */
  public  SetDiff1D setDiff1D(Operand x, Operand y,
      Class outIdx) {
    return SetDiff1D.create(scope, x, y, outIdx);
  }

  /**
   * Adds an {@link Empty} operation to the graph
   *
   * @param shape 1-D. Represents the shape of the output tensor.
   * @param dtype 
   * @param options carries optional attributes values
   * @return a new instance of Empty
   * @see {@link org.tensorflow.op.core.Empty}
   */
  public  Empty empty(Operand shape, Class dtype, Empty.Options... options) {
    return Empty.create(scope, shape, dtype, options);
  }

  /**
   * Adds an {@link BroadcastDynamicShape} operation to the graph
   *
   * @param s0 
   * @param s1 
   * @return a new instance of BroadcastDynamicShape
   * @see {@link org.tensorflow.op.core.BroadcastDynamicShape}
   */
  public  BroadcastDynamicShape broadcastDynamicShape(Operand s0,
      Operand s1) {
    return BroadcastDynamicShape.create(scope, s0, s1);
  }

  /**
   * Adds an {@link NonMaxSuppressionV2} operation to the graph
   *
   * @param boxes A 2-D float tensor of shape `[num_boxes, 4]`.
   * @param scores A 1-D float tensor of shape `[num_boxes]` representing a single
   * @param maxOutputSize A scalar integer tensor representing the maximum number of
   * @param iouThreshold A 0-D float tensor representing the threshold for deciding whether
   * @return a new instance of NonMaxSuppressionV2
   * @see {@link org.tensorflow.op.core.NonMaxSuppressionV2}
   */
  public NonMaxSuppressionV2 nonMaxSuppressionV2(Operand boxes, Operand scores,
      Operand maxOutputSize, Operand iouThreshold) {
    return NonMaxSuppressionV2.create(scope, boxes, scores, maxOutputSize, iouThreshold);
  }

  /**
   * Adds an {@link ResourceSparseApplyMomentum} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param lr Learning rate. Must be a scalar.
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param momentum Momentum. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of ResourceSparseApplyMomentum
   * @see {@link org.tensorflow.op.core.ResourceSparseApplyMomentum}
   */
  public  ResourceSparseApplyMomentum resourceSparseApplyMomentum(Operand var,
      Operand accum, Operand lr, Operand grad, Operand indices, Operand momentum,
      ResourceSparseApplyMomentum.Options... options) {
    return ResourceSparseApplyMomentum.create(scope, var, accum, lr, grad, indices, momentum, options);
  }

  /**
   * Adds an {@link Range} operation to the graph
   *
   * @param start 0-D (scalar). First entry in the sequence.
   * @param limit 0-D (scalar). Upper limit of sequence, exclusive.
   * @param delta 0-D (scalar). Optional. Default is 1. Number that increments `start`.
   * @return a new instance of Range
   * @see {@link org.tensorflow.op.core.Range}
   */
  public  Range range(Operand start, Operand limit, Operand delta) {
    return Range.create(scope, start, limit, delta);
  }

  /**
   * Adds an {@link SerializeTensor} operation to the graph
   *
   * @param tensor A Tensor of type `T`.
   * @return a new instance of SerializeTensor
   * @see {@link org.tensorflow.op.core.SerializeTensor}
   */
  public  SerializeTensor serializeTensor(Operand tensor) {
    return SerializeTensor.create(scope, tensor);
  }

  /**
   * Adds an {@link BiasAdd} operation to the graph
   *
   * @param value Any number of dimensions.
   * @param bias 1-D with size the last dimension of `value`.
   * @param options carries optional attributes values
   * @return a new instance of BiasAdd
   * @see {@link org.tensorflow.op.core.BiasAdd}
   */
  public  BiasAdd biasAdd(Operand value, Operand bias, BiasAdd.Options... options) {
    return BiasAdd.create(scope, value, bias, options);
  }

  /**
   * Adds an {@link Erf} operation to the graph
   *
   * @param x 
   * @return a new instance of Erf
   * @see {@link org.tensorflow.op.core.Erf}
   */
  public  Erf erf(Operand x) {
    return Erf.create(scope, x);
  }

  /**
   * Adds an {@link EncodeJpeg} operation to the graph
   *
   * @param image 3-D with shape `[height, width, channels]`.
   * @param options carries optional attributes values
   * @return a new instance of EncodeJpeg
   * @see {@link org.tensorflow.op.core.EncodeJpeg}
   */
  public EncodeJpeg encodeJpeg(Operand image, EncodeJpeg.Options... options) {
    return EncodeJpeg.create(scope, image, options);
  }

  /**
   * Adds an {@link StatelessRandomNormal} operation to the graph
   *
   * @param shape The shape of the output tensor.
   * @param seed 2 seeds (shape [2]).
   * @param dtype The type of the output.
   * @return a new instance of StatelessRandomNormal
   * @see {@link org.tensorflow.op.core.StatelessRandomNormal}
   */
  public  StatelessRandomNormal statelessRandomNormal(Operand shape,
      Operand seed, Class dtype) {
    return StatelessRandomNormal.create(scope, shape, seed, dtype);
  }

  /**
   * Adds an {@link IFFT3D} operation to the graph
   *
   * @param input A complex64 tensor.
   * @return a new instance of IFFT3D
   * @see {@link org.tensorflow.op.core.IFFT3D}
   */
  public  IFFT3D iFFT3D(Operand input) {
    return IFFT3D.create(scope, input);
  }

  /**
   * Adds an {@link FusedBatchNormGradV2} operation to the graph
   *
   * @param yBackprop A 4D Tensor for the gradient with respect to y.
   * @param x A 4D Tensor for input data.
   * @param scale A 1D Tensor for scaling factor, to scale the normalized x.
   * @param reserveSpace1 When is_training is True, a 1D Tensor for the computed batch
   * @param reserveSpace2 When is_training is True, a 1D Tensor for the computed batch
   * @param options carries optional attributes values
   * @return a new instance of FusedBatchNormGradV2
   * @see {@link org.tensorflow.op.core.FusedBatchNormGradV2}
   */
  public  FusedBatchNormGradV2 fusedBatchNormGradV2(Operand yBackprop,
      Operand x, Operand scale, Operand reserveSpace1, Operand reserveSpace2,
      FusedBatchNormGradV2.Options... options) {
    return FusedBatchNormGradV2.create(scope, yBackprop, x, scale, reserveSpace1, reserveSpace2, options);
  }

  /**
   * Adds an {@link SparseReorder} operation to the graph
   *
   * @param inputIndices 2-D.  `N x R` matrix with the indices of non-empty values in a
   * @param inputValues 1-D.  `N` non-empty values corresponding to `input_indices`.
   * @param inputShape 1-D.  Shape of the input SparseTensor.
   * @return a new instance of SparseReorder
   * @see {@link org.tensorflow.op.core.SparseReorder}
   */
  public  SparseReorder sparseReorder(Operand inputIndices, Operand inputValues,
      Operand inputShape) {
    return SparseReorder.create(scope, inputIndices, inputValues, inputShape);
  }

  /**
   * Adds an {@link MutableDenseHashTable} operation to the graph
   *
   * @param emptyKey The key used to represent empty key buckets internally. Must not
   * @param valueDtype Type of the table values.
   * @param options carries optional attributes values
   * @return a new instance of MutableDenseHashTable
   * @see {@link org.tensorflow.op.core.MutableDenseHashTable}
   */
  public  MutableDenseHashTable mutableDenseHashTable(Operand emptyKey,
      Class valueDtype, MutableDenseHashTable.Options... options) {
    return MutableDenseHashTable.create(scope, emptyKey, valueDtype, options);
  }

  /**
   * Adds an {@link RandomCrop} operation to the graph
   *
   * @param image 3-D of shape `[height, width, channels]`.
   * @param size 1-D of length 2 containing: `crop_height`, `crop_width`..
   * @param options carries optional attributes values
   * @return a new instance of RandomCrop
   * @see {@link org.tensorflow.op.core.RandomCrop}
   */
  public  RandomCrop randomCrop(Operand image, Operand size,
      RandomCrop.Options... options) {
    return RandomCrop.create(scope, image, size, options);
  }

  /**
   * Adds an {@link FFT2D} operation to the graph
   *
   * @param input A complex64 tensor.
   * @return a new instance of FFT2D
   * @see {@link org.tensorflow.op.core.FFT2D}
   */
  public  FFT2D fFT2D(Operand input) {
    return FFT2D.create(scope, input);
  }

  /**
   * Adds an {@link SparseFillEmptyRows} operation to the graph
   *
   * @param indices 2-D. the indices of the sparse tensor.
   * @param values 1-D. the values of the sparse tensor.
   * @param denseShape 1-D. the shape of the sparse tensor.
   * @param defaultValue 0-D. default value to insert into location `[row, 0, ..., 0]`
   * @return a new instance of SparseFillEmptyRows
   * @see {@link org.tensorflow.op.core.SparseFillEmptyRows}
   */
  public  SparseFillEmptyRows sparseFillEmptyRows(Operand indices, Operand values,
      Operand denseShape, Operand defaultValue) {
    return SparseFillEmptyRows.create(scope, indices, values, denseShape, defaultValue);
  }

  /**
   * Adds an {@link ScatterUpdate} operation to the graph
   *
   * @param ref Should be from a `Variable` node.
   * @param indices A tensor of indices into the first dimension of `ref`.
   * @param updates A tensor of updated values to store in `ref`.
   * @param options carries optional attributes values
   * @return a new instance of ScatterUpdate
   * @see {@link org.tensorflow.op.core.ScatterUpdate}
   */
  public  ScatterUpdate scatterUpdate(Operand ref, Operand indices,
      Operand updates, ScatterUpdate.Options... options) {
    return ScatterUpdate.create(scope, ref, indices, updates, options);
  }

  /**
   * Adds an {@link AccumulatorApplyGradient} operation to the graph
   *
   * @param handle The handle to a accumulator.
   * @param localStep The local_step value at which the gradient was computed.
   * @param gradient A tensor of the gradient to be accumulated.
   * @return a new instance of AccumulatorApplyGradient
   * @see {@link org.tensorflow.op.core.AccumulatorApplyGradient}
   */
  public  AccumulatorApplyGradient accumulatorApplyGradient(Operand handle,
      Operand localStep, Operand gradient) {
    return AccumulatorApplyGradient.create(scope, handle, localStep, gradient);
  }

  /**
   * Adds an {@link DecodeBase64} operation to the graph
   *
   * @param input Base64 strings to decode.
   * @return a new instance of DecodeBase64
   * @see {@link org.tensorflow.op.core.DecodeBase64}
   */
  public DecodeBase64 decodeBase64(Operand input) {
    return DecodeBase64.create(scope, input);
  }

  /**
   * Adds an {@link Split} operation to the graph
   *
   * @param axis 0-D.  The dimension along which to split.  Must be in the range
   * @param value The tensor to split.
   * @param numSplit The number of ways to split.  Must evenly divide
   * @return a new instance of Split
   * @see {@link org.tensorflow.op.core.Split}
   */
  public  Split split(Operand axis, Operand value, Long numSplit) {
    return Split.create(scope, axis, value, numSplit);
  }

  /**
   * Adds an {@link VariableShape} operation to the graph
   *
   * @param input 
   * @param outType 
   * @return a new instance of VariableShape
   * @see {@link org.tensorflow.op.core.VariableShape}
   */
  public  VariableShape variableShape(Operand input, Class outType) {
    return VariableShape.create(scope, input, outType);
  }

  /**
   * Adds an {@link Zeta} operation to the graph
   *
   * @param x 
   * @param q 
   * @return a new instance of Zeta
   * @see {@link org.tensorflow.op.core.Zeta}
   */
  public  Zeta zeta(Operand x, Operand q) {
    return Zeta.create(scope, x, q);
  }

  /**
   * Adds an {@link ResourceScatterMax} operation to the graph
   *
   * @param resource Should be from a `Variable` node.
   * @param indices A tensor of indices into the first dimension of `ref`.
   * @param updates A tensor of updated values to add to `ref`.
   * @return a new instance of ResourceScatterMax
   * @see {@link org.tensorflow.op.core.ResourceScatterMax}
   */
  public  ResourceScatterMax resourceScatterMax(Operand resource,
      Operand indices, Operand updates) {
    return ResourceScatterMax.create(scope, resource, indices, updates);
  }

  /**
   * Adds an {@link Shape} operation to the graph
   *
   * @param input 
   * @param outType 
   * @return a new instance of Shape
   * @see {@link org.tensorflow.op.core.Shape}
   */
  public  org.tensorflow.op.core.Shape shape(Operand input,
      Class outType) {
    return org.tensorflow.op.core.Shape.create(scope, input, outType);
  }

  /**
   * Adds an {@link TensorListStack} operation to the graph
   *
   * @param inputHandle 
   * @param elementDtype 
   * @param options carries optional attributes values
   * @return a new instance of TensorListStack
   * @see {@link org.tensorflow.op.core.TensorListStack}
   */
  public  TensorListStack tensorListStack(Operand inputHandle, Class elementDtype,
      TensorListStack.Options... options) {
    return TensorListStack.create(scope, inputHandle, elementDtype, options);
  }

  /**
   * Adds an {@link ExtractImagePatches} operation to the graph
   *
   * @param images 4-D Tensor with shape `[batch, in_rows, in_cols, depth]`.
   * @param ksizes The size of the sliding window for each dimension of `images`.
   * @param strides 1-D of length 4. How far the centers of two consecutive patches are in
   * @param rates 1-D of length 4. Must be: `[1, rate_rows, rate_cols, 1]`. This is the
   * @param padding The type of padding algorithm to use.
   * @return a new instance of ExtractImagePatches
   * @see {@link org.tensorflow.op.core.ExtractImagePatches}
   */
  public  ExtractImagePatches extractImagePatches(Operand images,
      List ksizes, List strides, List rates, String padding) {
    return ExtractImagePatches.create(scope, images, ksizes, strides, rates, padding);
  }

  /**
   * Adds an {@link ResourceSparseApplyRMSProp} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param ms Should be from a Variable().
   * @param mom Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param rho Decay rate. Must be a scalar.
   * @param momentum 
   * @param epsilon Ridge term. Must be a scalar.
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var, ms and mom.
   * @param options carries optional attributes values
   * @return a new instance of ResourceSparseApplyRMSProp
   * @see {@link org.tensorflow.op.core.ResourceSparseApplyRMSProp}
   */
  public  ResourceSparseApplyRMSProp resourceSparseApplyRMSProp(Operand var,
      Operand ms, Operand mom, Operand lr, Operand rho, Operand momentum,
      Operand epsilon, Operand grad, Operand indices,
      ResourceSparseApplyRMSProp.Options... options) {
    return ResourceSparseApplyRMSProp.create(scope, var, ms, mom, lr, rho, momentum, epsilon, grad, indices, options);
  }

  /**
   * Adds an {@link UnbatchDataset} operation to the graph
   *
   * @param inputDataset 
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of UnbatchDataset
   * @see {@link org.tensorflow.op.core.UnbatchDataset}
   */
  public UnbatchDataset unbatchDataset(Operand inputDataset, List> outputTypes,
      List outputShapes) {
    return UnbatchDataset.create(scope, inputDataset, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link MaxPool} operation to the graph
   *
   * @param input 4-D input to pool over.
   * @param ksize The size of the window for each dimension of the input tensor.
   * @param strides The stride of the sliding window for each dimension of the
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of MaxPool
   * @see {@link org.tensorflow.op.core.MaxPool}
   */
  public  MaxPool maxPool(Operand input, List ksize, List strides,
      String padding, MaxPool.Options... options) {
    return MaxPool.create(scope, input, ksize, strides, padding, options);
  }

  /**
   * Adds an {@link TruncatedNormal} operation to the graph
   *
   * @param shape The shape of the output tensor.
   * @param dtype The type of the output.
   * @param options carries optional attributes values
   * @return a new instance of TruncatedNormal
   * @see {@link org.tensorflow.op.core.TruncatedNormal}
   */
  public  TruncatedNormal truncatedNormal(Operand shape,
      Class dtype, TruncatedNormal.Options... options) {
    return TruncatedNormal.create(scope, shape, dtype, options);
  }

  /**
   * Adds an {@link MaxPool3DGrad} operation to the graph
   *
   * @param origInput The original input tensor.
   * @param origOutput The original output tensor.
   * @param grad Output backprop of shape `[batch, depth, rows, cols, channels]`.
   * @param ksize 1-D tensor of length 5. The size of the window for each dimension of
   * @param strides 1-D tensor of length 5. The stride of the sliding window for each
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of MaxPool3DGrad
   * @see {@link org.tensorflow.op.core.MaxPool3DGrad}
   */
  public  MaxPool3DGrad maxPool3DGrad(Operand origInput,
      Operand origOutput, Operand grad, List ksize, List strides, String padding,
      MaxPool3DGrad.Options... options) {
    return MaxPool3DGrad.create(scope, origInput, origOutput, grad, ksize, strides, padding, options);
  }

  /**
   * Adds an {@link SparseApplyMomentum} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param lr Learning rate. Must be a scalar.
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param momentum Momentum. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of SparseApplyMomentum
   * @see {@link org.tensorflow.op.core.SparseApplyMomentum}
   */
  public  SparseApplyMomentum sparseApplyMomentum(Operand var,
      Operand accum, Operand lr, Operand grad, Operand indices, Operand momentum,
      SparseApplyMomentum.Options... options) {
    return SparseApplyMomentum.create(scope, var, accum, lr, grad, indices, momentum, options);
  }

  /**
   * Adds an {@link ParallelConcat} operation to the graph
   *
   * @param values Tensors to be concatenated. All must have size 1 in the first dimension
   * @param shape the final shape of the result; should be equal to the shapes of any input
   * @return a new instance of ParallelConcat
   * @see {@link org.tensorflow.op.core.ParallelConcat}
   */
  public  ParallelConcat parallelConcat(Operand values, Shape shape) {
    return ParallelConcat.create(scope, values, shape);
  }

  /**
   * Adds an {@link LRN} operation to the graph
   *
   * @param input 4-D.
   * @param options carries optional attributes values
   * @return a new instance of LRN
   * @see {@link org.tensorflow.op.core.LRN}
   */
  public  LRN lRN(Operand input, LRN.Options... options) {
    return LRN.create(scope, input, options);
  }

  /**
   * Adds an {@link BroadcastTo} operation to the graph
   *
   * @param input A Tensor to broadcast.
   * @param shape An 1-D `int` Tensor. The shape of the desired output.
   * @return a new instance of BroadcastTo
   * @see {@link org.tensorflow.op.core.BroadcastTo}
   */
  public  BroadcastTo broadcastTo(Operand input, Operand shape) {
    return BroadcastTo.create(scope, input, shape);
  }

  /**
   * Adds an {@link SparseAccumulatorTakeGradient} operation to the graph
   *
   * @param handle The handle to a SparseConditionalAccumulator.
   * @param numRequired Number of gradients required before we return an aggregate.
   * @param dtype The data type of accumulated gradients. Needs to correspond to the type
   * @return a new instance of SparseAccumulatorTakeGradient
   * @see {@link org.tensorflow.op.core.SparseAccumulatorTakeGradient}
   */
  public  SparseAccumulatorTakeGradient sparseAccumulatorTakeGradient(Operand handle,
      Operand numRequired, Class dtype) {
    return SparseAccumulatorTakeGradient.create(scope, handle, numRequired, dtype);
  }

  /**
   * Adds an {@link AudioSummary} operation to the graph
   *
   * @param tag Scalar. Used to build the `tag` attribute of the summary values.
   * @param tensor 2-D of shape `[batch_size, frames]`.
   * @param sampleRate The sample rate of the signal in hertz.
   * @param options carries optional attributes values
   * @return a new instance of AudioSummary
   * @see {@link org.tensorflow.op.core.AudioSummary}
   */
  public AudioSummary audioSummary(Operand tag, Operand tensor,
      Operand sampleRate, AudioSummary.Options... options) {
    return AudioSummary.create(scope, tag, tensor, sampleRate, options);
  }

  /**
   * Adds an {@link DepthToSpace} operation to the graph
   *
   * @param input 
   * @param blockSize The size of the spatial block, same as in Space2Depth.
   * @param options carries optional attributes values
   * @return a new instance of DepthToSpace
   * @see {@link org.tensorflow.op.core.DepthToSpace}
   */
  public  DepthToSpace depthToSpace(Operand input, Long blockSize,
      DepthToSpace.Options... options) {
    return DepthToSpace.create(scope, input, blockSize, options);
  }

  /**
   * Adds an {@link BatchCholeskyGrad} operation to the graph
   *
   * @param l 
   * @param grad 
   * @return a new instance of BatchCholeskyGrad
   * @see {@link org.tensorflow.op.core.BatchCholeskyGrad}
   */
  public  BatchCholeskyGrad batchCholeskyGrad(Operand l, Operand grad) {
    return BatchCholeskyGrad.create(scope, l, grad);
  }

  /**
   * Adds an {@link GcsConfigureBlockCache} operation to the graph
   *
   * @param maxCacheSize 
   * @param blockSize 
   * @param maxStaleness 
   * @return a new instance of GcsConfigureBlockCache
   * @see {@link org.tensorflow.op.core.GcsConfigureBlockCache}
   */
  public GcsConfigureBlockCache gcsConfigureBlockCache(Operand maxCacheSize,
      Operand blockSize, Operand maxStaleness) {
    return GcsConfigureBlockCache.create(scope, maxCacheSize, blockSize, maxStaleness);
  }

  /**
   * Adds an {@link Dilation2DBackpropInput} operation to the graph
   *
   * @param input 4-D with shape `[batch, in_height, in_width, depth]`.
   * @param filter 3-D with shape `[filter_height, filter_width, depth]`.
   * @param outBackprop 4-D with shape `[batch, out_height, out_width, depth]`.
   * @param strides 1-D of length 4. The stride of the sliding window for each dimension of
   * @param rates 1-D of length 4. The input stride for atrous morphological dilation.
   * @param padding The type of padding algorithm to use.
   * @return a new instance of Dilation2DBackpropInput
   * @see {@link org.tensorflow.op.core.Dilation2DBackpropInput}
   */
  public  Dilation2DBackpropInput dilation2DBackpropInput(Operand input,
      Operand filter, Operand outBackprop, List strides, List rates,
      String padding) {
    return Dilation2DBackpropInput.create(scope, input, filter, outBackprop, strides, rates, padding);
  }

  /**
   * Adds an {@link StridedSlice} operation to the graph
   *
   * @param input 
   * @param begin `begin[k]` specifies the offset into the `k`th range specification.
   * @param end `end[i]` is like `begin` with the exception that `end_mask` is
   * @param strides `strides[i]` specifies the increment in the `i`th specification
   * @param options carries optional attributes values
   * @return a new instance of StridedSlice
   * @see {@link org.tensorflow.op.core.StridedSlice}
   */
  public  StridedSlice stridedSlice(Operand input, Operand begin,
      Operand end, Operand strides, StridedSlice.Options... options) {
    return StridedSlice.create(scope, input, begin, end, strides, options);
  }

  /**
   * Adds an {@link UniqueWithCountsV2} operation to the graph
   *
   * @param x A `Tensor`.
   * @param axis A `Tensor` of type `int32` (default: None). The axis of the Tensor to
   * @param outIdx 
   * @return a new instance of UniqueWithCountsV2
   * @see {@link org.tensorflow.op.core.UniqueWithCountsV2}
   */
  public  UniqueWithCountsV2 uniqueWithCountsV2(Operand x,
      Operand axis, Class outIdx) {
    return UniqueWithCountsV2.create(scope, x, axis, outIdx);
  }

  /**
   * Adds an {@link OrderedMapUnstageNoKey} operation to the graph
   *
   * @param indices 
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of OrderedMapUnstageNoKey
   * @see {@link org.tensorflow.op.core.OrderedMapUnstageNoKey}
   */
  public OrderedMapUnstageNoKey orderedMapUnstageNoKey(Operand indices,
      List> dtypes, OrderedMapUnstageNoKey.Options... options) {
    return OrderedMapUnstageNoKey.create(scope, indices, dtypes, options);
  }

  /**
   * Adds an {@link SparseReduceSumSparse} operation to the graph
   *
   * @param inputIndices 2-D.  `N x R` matrix with the indices of non-empty values in a
   * @param inputValues 1-D.  `N` non-empty values corresponding to `input_indices`.
   * @param inputShape 1-D.  Shape of the input SparseTensor.
   * @param reductionAxes 1-D.  Length-`K` vector containing the reduction axes.
   * @param options carries optional attributes values
   * @return a new instance of SparseReduceSumSparse
   * @see {@link org.tensorflow.op.core.SparseReduceSumSparse}
   */
  public  SparseReduceSumSparse sparseReduceSumSparse(Operand inputIndices,
      Operand inputValues, Operand inputShape, Operand reductionAxes,
      SparseReduceSumSparse.Options... options) {
    return SparseReduceSumSparse.create(scope, inputIndices, inputValues, inputShape, reductionAxes, options);
  }

  /**
   * Adds an {@link ResizeNearestNeighbor} operation to the graph
   *
   * @param images 4-D with shape `[batch, height, width, channels]`.
   * @param size = A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
   * @param options carries optional attributes values
   * @return a new instance of ResizeNearestNeighbor
   * @see {@link org.tensorflow.op.core.ResizeNearestNeighbor}
   */
  public  ResizeNearestNeighbor resizeNearestNeighbor(Operand images,
      Operand size, ResizeNearestNeighbor.Options... options) {
    return ResizeNearestNeighbor.create(scope, images, size, options);
  }

  /**
   * Adds an {@link FakeQuantWithMinMaxVarsPerChannel} operation to the graph
   *
   * @param inputs 
   * @param min 
   * @param max 
   * @param options carries optional attributes values
   * @return a new instance of FakeQuantWithMinMaxVarsPerChannel
   * @see {@link org.tensorflow.op.core.FakeQuantWithMinMaxVarsPerChannel}
   */
  public FakeQuantWithMinMaxVarsPerChannel fakeQuantWithMinMaxVarsPerChannel(Operand inputs,
      Operand min, Operand max,
      FakeQuantWithMinMaxVarsPerChannel.Options... options) {
    return FakeQuantWithMinMaxVarsPerChannel.create(scope, inputs, min, max, options);
  }

  /**
   * Adds an {@link TileGrad} operation to the graph
   *
   * @param input 
   * @param multiples 
   * @return a new instance of TileGrad
   * @see {@link org.tensorflow.op.core.TileGrad}
   */
  public  TileGrad tileGrad(Operand input, Operand multiples) {
    return TileGrad.create(scope, input, multiples);
  }

  /**
   * Adds an {@link FusedBatchNormV2} operation to the graph
   *
   * @param x A 4D Tensor for input data.
   * @param scale A 1D Tensor for scaling factor, to scale the normalized x.
   * @param offset A 1D Tensor for offset, to shift to the normalized x.
   * @param mean A 1D Tensor for population mean. Used for inference only;
   * @param variance A 1D Tensor for population variance. Used for inference only;
   * @param options carries optional attributes values
   * @return a new instance of FusedBatchNormV2
   * @see {@link org.tensorflow.op.core.FusedBatchNormV2}
   */
  public  FusedBatchNormV2 fusedBatchNormV2(Operand x,
      Operand scale, Operand offset, Operand mean, Operand variance,
      FusedBatchNormV2.Options... options) {
    return FusedBatchNormV2.create(scope, x, scale, offset, mean, variance, options);
  }

  /**
   * Adds an {@link Variable} operation to the graph
   *
   * @param shape The shape of the variable tensor.
   * @param dtype The type of elements in the variable tensor.
   * @param options carries optional attributes values
   * @return a new instance of Variable
   * @see {@link org.tensorflow.op.core.Variable}
   */
  public  Variable variable(Shape shape, Class dtype, Variable.Options... options) {
    return Variable.create(scope, shape, dtype, options);
  }

  /**
   * Adds an {@link OnesLike} operation to the graph
   *
   * @param x a tensor of type T.
   * @return a new instance of OnesLike
   * @see {@link org.tensorflow.op.core.OnesLike}
   */
  public  OnesLike onesLike(Operand x) {
    return OnesLike.create(scope, x);
  }

  /**
   * Adds an {@link ReaderSerializeState} operation to the graph
   *
   * @param readerHandle Handle to a Reader.
   * @return a new instance of ReaderSerializeState
   * @see {@link org.tensorflow.op.core.ReaderSerializeState}
   */
  public ReaderSerializeState readerSerializeState(Operand readerHandle) {
    return ReaderSerializeState.create(scope, readerHandle);
  }

  /**
   * Adds an {@link TensorArrayConcat} operation to the graph
   *
   * @param handle The handle to a TensorArray.
   * @param flowIn A float scalar that enforces proper chaining of operations.
   * @param dtype The type of the elem that is returned.
   * @param options carries optional attributes values
   * @return a new instance of TensorArrayConcat
   * @see {@link org.tensorflow.op.core.TensorArrayConcat}
   */
  public  TensorArrayConcat tensorArrayConcat(Operand handle, Operand flowIn,
      Class dtype, TensorArrayConcat.Options... options) {
    return TensorArrayConcat.create(scope, handle, flowIn, dtype, options);
  }

  /**
   * Adds an {@link Sub} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of Sub
   * @see {@link org.tensorflow.op.core.Sub}
   */
  public  Sub sub(Operand x, Operand y) {
    return Sub.create(scope, x, y);
  }

  /**
   * Adds an {@link DecodeProtoV2} operation to the graph
   *
   * @param bytes Tensor of serialized protos with shape `batch_shape`.
   * @param messageType Name of the proto message type to decode.
   * @param fieldNames List of strings containing proto field names.
   * @param outputTypes List of TF types to use for the respective field in field_names.
   * @param options carries optional attributes values
   * @return a new instance of DecodeProtoV2
   * @see {@link org.tensorflow.op.core.DecodeProtoV2}
   */
  public DecodeProtoV2 decodeProtoV2(Operand bytes, String messageType,
      List fieldNames, List> outputTypes, DecodeProtoV2.Options... options) {
    return DecodeProtoV2.create(scope, bytes, messageType, fieldNames, outputTypes, options);
  }

  /**
   * Adds an {@link SparseSplit} operation to the graph
   *
   * @param splitDim 0-D.  The dimension along which to split.  Must be in the range
   * @param indices 2-D tensor represents the indices of the sparse tensor.
   * @param values 1-D tensor represents the values of the sparse tensor.
   * @param shape 1-D. tensor represents the shape of the sparse tensor.
   * @param numSplit The number of ways to split.
   * @return a new instance of SparseSplit
   * @see {@link org.tensorflow.op.core.SparseSplit}
   */
  public  SparseSplit sparseSplit(Operand splitDim, Operand indices,
      Operand values, Operand shape, Long numSplit) {
    return SparseSplit.create(scope, splitDim, indices, values, shape, numSplit);
  }

  /**
   * Adds an {@link AssignSub} operation to the graph
   *
   * @param ref Should be from a `Variable` node.
   * @param value The value to be subtracted to the variable.
   * @param options carries optional attributes values
   * @return a new instance of AssignSub
   * @see {@link org.tensorflow.op.core.AssignSub}
   */
  public  AssignSub assignSub(Operand ref, Operand value,
      AssignSub.Options... options) {
    return AssignSub.create(scope, ref, value, options);
  }

  /**
   * Adds an {@link ImmutableConst} operation to the graph
   *
   * @param dtype Type of the returned tensor.
   * @param shape Shape of the returned tensor.
   * @param memoryRegionName Name of readonly memory region used by the tensor, see
   * @return a new instance of ImmutableConst
   * @see {@link org.tensorflow.op.core.ImmutableConst}
   */
  public  ImmutableConst immutableConst(Class dtype, Shape shape,
      String memoryRegionName) {
    return ImmutableConst.create(scope, dtype, shape, memoryRegionName);
  }

  /**
   * Adds an {@link CTCLoss} operation to the graph
   *
   * @param inputs 3-D, shape: `(max_time x batch_size x num_classes)`, the logits.
   * @param labelsIndices The indices of a `SparseTensor`.
   * @param labelsValues The values (labels) associated with the given batch and time.
   * @param sequenceLength A vector containing sequence lengths (batch).
   * @param options carries optional attributes values
   * @return a new instance of CTCLoss
   * @see {@link org.tensorflow.op.core.CTCLoss}
   */
  public CTCLoss cTCLoss(Operand inputs, Operand labelsIndices,
      Operand labelsValues, Operand sequenceLength, CTCLoss.Options... options) {
    return CTCLoss.create(scope, inputs, labelsIndices, labelsValues, sequenceLength, options);
  }

  /**
   * Adds an {@link BesselI1e} operation to the graph
   *
   * @param x 
   * @return a new instance of BesselI1e
   * @see {@link org.tensorflow.op.core.BesselI1e}
   */
  public  BesselI1e besselI1e(Operand x) {
    return BesselI1e.create(scope, x);
  }

  /**
   * Adds an {@link ResourceScatterSub} operation to the graph
   *
   * @param resource Should be from a `Variable` node.
   * @param indices A tensor of indices into the first dimension of `ref`.
   * @param updates A tensor of updated values to add to `ref`.
   * @return a new instance of ResourceScatterSub
   * @see {@link org.tensorflow.op.core.ResourceScatterSub}
   */
  public  ResourceScatterSub resourceScatterSub(Operand resource,
      Operand indices, Operand updates) {
    return ResourceScatterSub.create(scope, resource, indices, updates);
  }

  /**
   * Adds an {@link UnsortedSegmentProd} operation to the graph
   *
   * @param data 
   * @param segmentIds A 1-D tensor whose rank is equal to the rank of `data`'s
   * @param numSegments 
   * @return a new instance of UnsortedSegmentProd
   * @see {@link org.tensorflow.op.core.UnsortedSegmentProd}
   */
  public  UnsortedSegmentProd unsortedSegmentProd(Operand data,
      Operand segmentIds, Operand numSegments) {
    return UnsortedSegmentProd.create(scope, data, segmentIds, numSegments);
  }

  /**
   * Adds an {@link GenerateVocabRemapping} operation to the graph
   *
   * @param newVocabFile Path to the new vocab file.
   * @param oldVocabFile Path to the old vocab file.
   * @param newVocabOffset How many entries into the new vocab file to start reading.
   * @param numNewVocab Number of entries in the new vocab file to remap.
   * @param options carries optional attributes values
   * @return a new instance of GenerateVocabRemapping
   * @see {@link org.tensorflow.op.core.GenerateVocabRemapping}
   */
  public GenerateVocabRemapping generateVocabRemapping(Operand newVocabFile,
      Operand oldVocabFile, Long newVocabOffset, Long numNewVocab,
      GenerateVocabRemapping.Options... options) {
    return GenerateVocabRemapping.create(scope, newVocabFile, oldVocabFile, newVocabOffset, numNewVocab, options);
  }

  /**
   * Adds an {@link UniqueWithCounts} operation to the graph
   *
   * @param x 1-D.
   * @param outIdx 
   * @return a new instance of UniqueWithCounts
   * @see {@link org.tensorflow.op.core.UniqueWithCounts}
   */
  public  UniqueWithCounts uniqueWithCounts(Operand x,
      Class outIdx) {
    return UniqueWithCounts.create(scope, x, outIdx);
  }

  /**
   * Adds an {@link OrderedMapSize} operation to the graph
   *
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of OrderedMapSize
   * @see {@link org.tensorflow.op.core.OrderedMapSize}
   */
  public OrderedMapSize orderedMapSize(List> dtypes, OrderedMapSize.Options... options) {
    return OrderedMapSize.create(scope, dtypes, options);
  }

  /**
   * Adds an {@link Rint} operation to the graph
   *
   * @param x 
   * @return a new instance of Rint
   * @see {@link org.tensorflow.op.core.Rint}
   */
  public  Rint rint(Operand x) {
    return Rint.create(scope, x);
  }

  /**
   * Adds an {@link Negate} operation to the graph
   *
   * @param x 
   * @return a new instance of Negate
   * @see {@link org.tensorflow.op.core.Negate}
   */
  public  Negate negate(Operand x) {
    return Negate.create(scope, x);
  }

  /**
   * Adds an {@link GenerateBigQueryReaderPartitions} operation to the graph
   *
   * @param projectId GCP project ID.
   * @param datasetId BigQuery Dataset ID.
   * @param tableId Table to read.
   * @param columns List of columns to read. Leave empty to read all columns.
   * @param timestampMillis Table snapshot timestamp in millis since epoch. Relative
   * @param numPartitions Number of partitions to split the table into.
   * @param options carries optional attributes values
   * @return a new instance of GenerateBigQueryReaderPartitions
   * @see {@link org.tensorflow.op.core.GenerateBigQueryReaderPartitions}
   */
  public GenerateBigQueryReaderPartitions generateBigQueryReaderPartitions(String projectId,
      String datasetId, String tableId, List columns, Long timestampMillis,
      Long numPartitions, GenerateBigQueryReaderPartitions.Options... options) {
    return GenerateBigQueryReaderPartitions.create(scope, projectId, datasetId, tableId, columns, timestampMillis, numPartitions, options);
  }

  /**
   * Adds an {@link RegexReplace} operation to the graph
   *
   * @param input The text to be processed.
   * @param pattern The regular expression to match the input.
   * @param rewrite The rewrite to be applied to the matched expresion.
   * @param options carries optional attributes values
   * @return a new instance of RegexReplace
   * @see {@link org.tensorflow.op.core.RegexReplace}
   */
  public RegexReplace regexReplace(Operand input, Operand pattern,
      Operand rewrite, RegexReplace.Options... options) {
    return RegexReplace.create(scope, input, pattern, rewrite, options);
  }

  /**
   * Adds an {@link ScatterMin} operation to the graph
   *
   * @param ref Should be from a `Variable` node.
   * @param indices A tensor of indices into the first dimension of `ref`.
   * @param updates A tensor of updated values to reduce into `ref`.
   * @param options carries optional attributes values
   * @return a new instance of ScatterMin
   * @see {@link org.tensorflow.op.core.ScatterMin}
   */
  public  ScatterMin scatterMin(Operand ref,
      Operand indices, Operand updates, ScatterMin.Options... options) {
    return ScatterMin.create(scope, ref, indices, updates, options);
  }

  /**
   * Adds an {@link IteratorGetNextSync} operation to the graph
   *
   * @param iterator 
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of IteratorGetNextSync
   * @see {@link org.tensorflow.op.core.IteratorGetNextSync}
   */
  public IteratorGetNextSync iteratorGetNextSync(Operand iterator, List> outputTypes,
      List outputShapes) {
    return IteratorGetNextSync.create(scope, iterator, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link BarrierReadySize} operation to the graph
   *
   * @param handle The handle to a barrier.
   * @return a new instance of BarrierReadySize
   * @see {@link org.tensorflow.op.core.BarrierReadySize}
   */
  public BarrierReadySize barrierReadySize(Operand handle) {
    return BarrierReadySize.create(scope, handle);
  }

  /**
   * Adds an {@link IsNan} operation to the graph
   *
   * @param x 
   * @return a new instance of IsNan
   * @see {@link org.tensorflow.op.core.IsNan}
   */
  public  IsNan isNan(Operand x) {
    return IsNan.create(scope, x);
  }

  /**
   * Adds an {@link Asin} operation to the graph
   *
   * @param x 
   * @return a new instance of Asin
   * @see {@link org.tensorflow.op.core.Asin}
   */
  public  Asin asin(Operand x) {
    return Asin.create(scope, x);
  }

  /**
   * Adds an {@link CudnnRNN} operation to the graph
   *
   * @param input 
   * @param inputH 
   * @param inputC 
   * @param params 
   * @param options carries optional attributes values
   * @return a new instance of CudnnRNN
   * @see {@link org.tensorflow.op.core.CudnnRNN}
   */
  public  CudnnRNN cudnnRNN(Operand input, Operand inputH,
      Operand inputC, Operand params, CudnnRNN.Options... options) {
    return CudnnRNN.create(scope, input, inputH, inputC, params, options);
  }

  /**
   * Adds an {@link Reciprocal} operation to the graph
   *
   * @param x 
   * @return a new instance of Reciprocal
   * @see {@link org.tensorflow.op.core.Reciprocal}
   */
  public  Reciprocal reciprocal(Operand x) {
    return Reciprocal.create(scope, x);
  }

  /**
   * Adds an {@link TensorArrayPack} operation to the graph
   *
   * @param handle 
   * @param flowIn 
   * @param dtype 
   * @param options carries optional attributes values
   * @return a new instance of TensorArrayPack
   * @see {@link org.tensorflow.op.core.TensorArrayPack}
   */
  public  TensorArrayPack tensorArrayPack(Operand handle, Operand flowIn,
      Class dtype, TensorArrayPack.Options... options) {
    return TensorArrayPack.create(scope, handle, flowIn, dtype, options);
  }

  /**
   * Adds an {@link LookupTableInsert} operation to the graph
   *
   * @param tableHandle Handle to the table.
   * @param keys Any shape.  Keys to look up.
   * @param values Values to associate with keys.
   * @return a new instance of LookupTableInsert
   * @see {@link org.tensorflow.op.core.LookupTableInsert}
   */
  public  LookupTableInsert lookupTableInsert(Operand tableHandle, Operand keys,
      Operand values) {
    return LookupTableInsert.create(scope, tableHandle, keys, values);
  }

  /**
   * Adds an {@link ExtractJpegShape} operation to the graph
   *
   * @param contents 0-D. The JPEG-encoded image.
   * @param outputType (Optional) The output type of the operation (int32 or int64).
   * @return a new instance of ExtractJpegShape
   * @see {@link org.tensorflow.op.core.ExtractJpegShape}
   */
  public  ExtractJpegShape extractJpegShape(Operand contents,
      Class outputType) {
    return ExtractJpegShape.create(scope, contents, outputType);
  }

  /**
   * Adds an {@link MaxPool3D} operation to the graph
   *
   * @param input Shape `[batch, depth, rows, cols, channels]` tensor to pool over.
   * @param ksize 1-D tensor of length 5. The size of the window for each dimension of
   * @param strides 1-D tensor of length 5. The stride of the sliding window for each
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of MaxPool3D
   * @see {@link org.tensorflow.op.core.MaxPool3D}
   */
  public  MaxPool3D maxPool3D(Operand input, List ksize,
      List strides, String padding, MaxPool3D.Options... options) {
    return MaxPool3D.create(scope, input, ksize, strides, padding, options);
  }

  /**
   * Adds an {@link VarIsInitializedOp} operation to the graph
   *
   * @param resource the input resource handle.
   * @return a new instance of VarIsInitializedOp
   * @see {@link org.tensorflow.op.core.VarIsInitializedOp}
   */
  public VarIsInitializedOp varIsInitializedOp(Operand resource) {
    return VarIsInitializedOp.create(scope, resource);
  }

  /**
   * Adds an {@link Selu} operation to the graph
   *
   * @param features 
   * @return a new instance of Selu
   * @see {@link org.tensorflow.op.core.Selu}
   */
  public  Selu selu(Operand features) {
    return Selu.create(scope, features);
  }

  /**
   * Adds an {@link DecodeCSV} operation to the graph
   *
   * @param records Each string is a record/row in the csv and all records should have
   * @param recordDefaults One tensor per column of the input record, with either a
   * @param options carries optional attributes values
   * @return a new instance of DecodeCSV
   * @see {@link org.tensorflow.op.core.DecodeCSV}
   */
  public DecodeCSV decodeCSV(Operand records, Iterable> recordDefaults,
      DecodeCSV.Options... options) {
    return DecodeCSV.create(scope, records, recordDefaults, options);
  }

  /**
   * Adds an {@link DestroyResourceOp} operation to the graph
   *
   * @param resource handle to the resource to delete.
   * @param options carries optional attributes values
   * @return a new instance of DestroyResourceOp
   * @see {@link org.tensorflow.op.core.DestroyResourceOp}
   */
  public DestroyResourceOp destroyResourceOp(Operand resource,
      DestroyResourceOp.Options... options) {
    return DestroyResourceOp.create(scope, resource, options);
  }

  /**
   * Adds an {@link SparseSparseMinimum} operation to the graph
   *
   * @param aIndices 2-D.  `N x R` matrix with the indices of non-empty values in a
   * @param aValues 1-D.  `N` non-empty values corresponding to `a_indices`.
   * @param aShape 1-D.  Shape of the input SparseTensor.
   * @param bIndices counterpart to `a_indices` for the other operand.
   * @param bValues counterpart to `a_values` for the other operand; must be of the same dtype.
   * @param bShape counterpart to `a_shape` for the other operand; the two shapes must be equal.
   * @return a new instance of SparseSparseMinimum
   * @see {@link org.tensorflow.op.core.SparseSparseMinimum}
   */
  public  SparseSparseMinimum sparseSparseMinimum(Operand aIndices, Operand aValues,
      Operand aShape, Operand bIndices, Operand bValues, Operand bShape) {
    return SparseSparseMinimum.create(scope, aIndices, aValues, aShape, bIndices, bValues, bShape);
  }

  /**
   * Adds an {@link RandomGamma} operation to the graph
   *
   * @param shape 1-D integer tensor. Shape of independent samples to draw from each
   * @param alpha A tensor in which each scalar is a "shape" parameter describing the
   * @param options carries optional attributes values
   * @return a new instance of RandomGamma
   * @see {@link org.tensorflow.op.core.RandomGamma}
   */
  public  RandomGamma randomGamma(Operand shape,
      Operand alpha, RandomGamma.Options... options) {
    return RandomGamma.create(scope, shape, alpha, options);
  }

  /**
   * Adds an {@link MatrixDeterminant} operation to the graph
   *
   * @param input Shape is `[..., M, M]`.
   * @return a new instance of MatrixDeterminant
   * @see {@link org.tensorflow.op.core.MatrixDeterminant}
   */
  public  MatrixDeterminant matrixDeterminant(Operand input) {
    return MatrixDeterminant.create(scope, input);
  }

  /**
   * Adds an {@link MapSize} operation to the graph
   *
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of MapSize
   * @see {@link org.tensorflow.op.core.MapSize}
   */
  public MapSize mapSize(List> dtypes, MapSize.Options... options) {
    return MapSize.create(scope, dtypes, options);
  }

  /**
   * Adds an {@link ZerosLike} operation to the graph
   *
   * @param x a tensor of type T.
   * @return a new instance of ZerosLike
   * @see {@link org.tensorflow.op.core.ZerosLike}
   */
  public  ZerosLike zerosLike(Operand x) {
    return ZerosLike.create(scope, x);
  }

  /**
   * Adds an {@link Square} operation to the graph
   *
   * @param x 
   * @return a new instance of Square
   * @see {@link org.tensorflow.op.core.Square}
   */
  public  Square square(Operand x) {
    return Square.create(scope, x);
  }

  /**
   * Adds an {@link Sin} operation to the graph
   *
   * @param x 
   * @return a new instance of Sin
   * @see {@link org.tensorflow.op.core.Sin}
   */
  public  Sin sin(Operand x) {
    return Sin.create(scope, x);
  }

  /**
   * Adds an {@link ReaderNumRecordsProduced} operation to the graph
   *
   * @param readerHandle Handle to a Reader.
   * @return a new instance of ReaderNumRecordsProduced
   * @see {@link org.tensorflow.op.core.ReaderNumRecordsProduced}
   */
  public ReaderNumRecordsProduced readerNumRecordsProduced(Operand readerHandle) {
    return ReaderNumRecordsProduced.create(scope, readerHandle);
  }

  /**
   * Adds an {@link QuantizedAvgPool} operation to the graph
   *
   * @param input 4-D with shape `[batch, height, width, channels]`.
   * @param minInput The float value that the lowest quantized input value represents.
   * @param maxInput The float value that the highest quantized input value represents.
   * @param ksize The size of the window for each dimension of the input tensor.
   * @param strides The stride of the sliding window for each dimension of the input
   * @param padding The type of padding algorithm to use.
   * @return a new instance of QuantizedAvgPool
   * @see {@link org.tensorflow.op.core.QuantizedAvgPool}
   */
  public  QuantizedAvgPool quantizedAvgPool(Operand input, Operand minInput,
      Operand maxInput, List ksize, List strides, String padding) {
    return QuantizedAvgPool.create(scope, input, minInput, maxInput, ksize, strides, padding);
  }

  /**
   * Adds an {@link LookupTableImport} operation to the graph
   *
   * @param tableHandle Handle to the table.
   * @param keys Any shape.  Keys to look up.
   * @param values Values to associate with keys.
   * @return a new instance of LookupTableImport
   * @see {@link org.tensorflow.op.core.LookupTableImport}
   */
  public  LookupTableImport lookupTableImport(Operand tableHandle, Operand keys,
      Operand values) {
    return LookupTableImport.create(scope, tableHandle, keys, values);
  }

  /**
   * Adds an {@link SelfAdjointEig} operation to the graph
   *
   * @param input `Tensor` input of shape `[N, N]`.
   * @param options carries optional attributes values
   * @return a new instance of SelfAdjointEig
   * @see {@link org.tensorflow.op.core.SelfAdjointEig}
   */
  public  SelfAdjointEig selfAdjointEig(Operand input, SelfAdjointEig.Options... options) {
    return SelfAdjointEig.create(scope, input, options);
  }

  /**
   * Adds an {@link SampleDistortedBoundingBox} operation to the graph
   *
   * @param imageSize 1-D, containing `[height, width, channels]`.
   * @param boundingBoxes 3-D with shape `[batch, N, 4]` describing the N bounding boxes
   * @param options carries optional attributes values
   * @return a new instance of SampleDistortedBoundingBox
   * @see {@link org.tensorflow.op.core.SampleDistortedBoundingBox}
   */
  public  SampleDistortedBoundingBox sampleDistortedBoundingBox(Operand imageSize,
      Operand boundingBoxes, SampleDistortedBoundingBox.Options... options) {
    return SampleDistortedBoundingBox.create(scope, imageSize, boundingBoxes, options);
  }

  /**
   * Adds an {@link NonMaxSuppressionWithOverlaps} operation to the graph
   *
   * @param overlaps A 2-D float tensor of shape `[num_boxes, num_boxes]` representing
   * @param scores A 1-D float tensor of shape `[num_boxes]` representing a single
   * @param maxOutputSize A scalar integer tensor representing the maximum number of
   * @param overlapThreshold A 0-D float tensor representing the threshold for deciding whether
   * @param scoreThreshold A 0-D float tensor representing the threshold for deciding when to remove
   * @return a new instance of NonMaxSuppressionWithOverlaps
   * @see {@link org.tensorflow.op.core.NonMaxSuppressionWithOverlaps}
   */
  public NonMaxSuppressionWithOverlaps nonMaxSuppressionWithOverlaps(Operand overlaps,
      Operand scores, Operand maxOutputSize, Operand overlapThreshold,
      Operand scoreThreshold) {
    return NonMaxSuppressionWithOverlaps.create(scope, overlaps, scores, maxOutputSize, overlapThreshold, scoreThreshold);
  }

  /**
   * Adds an {@link BatchMatrixBandPart} operation to the graph
   *
   * @param input 
   * @param numLower 
   * @param numUpper 
   * @return a new instance of BatchMatrixBandPart
   * @see {@link org.tensorflow.op.core.BatchMatrixBandPart}
   */
  public  BatchMatrixBandPart batchMatrixBandPart(Operand input, Operand numLower,
      Operand numUpper) {
    return BatchMatrixBandPart.create(scope, input, numLower, numUpper);
  }

  /**
   * Adds an {@link SegmentMax} operation to the graph
   *
   * @param data 
   * @param segmentIds A 1-D tensor whose rank is equal to the rank of `data`'s
   * @return a new instance of SegmentMax
   * @see {@link org.tensorflow.op.core.SegmentMax}
   */
  public  SegmentMax segmentMax(Operand data,
      Operand segmentIds) {
    return SegmentMax.create(scope, data, segmentIds);
  }

  /**
   * Adds an {@link SetStatsAggregatorDataset} operation to the graph
   *
   * @param inputDataset 
   * @param statsAggregator 
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of SetStatsAggregatorDataset
   * @see {@link org.tensorflow.op.core.SetStatsAggregatorDataset}
   */
  public SetStatsAggregatorDataset setStatsAggregatorDataset(Operand inputDataset,
      Operand statsAggregator, List> outputTypes, List outputShapes) {
    return SetStatsAggregatorDataset.create(scope, inputDataset, statsAggregator, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link DynamicStitch} operation to the graph
   *
   * @param indices 
   * @param data 
   * @return a new instance of DynamicStitch
   * @see {@link org.tensorflow.op.core.DynamicStitch}
   */
  public  DynamicStitch dynamicStitch(Iterable> indices, Operand data) {
    return DynamicStitch.create(scope, indices, data);
  }

  /**
   * Adds an {@link Gradients} operation to the graph
   *
   * @param y output of the function to derive
   * @param x inputs of the function for which partial derivatives are computed
   * @param options carries optional attributes values
   * @return a new instance of {@code Gradients}
   * @see {@link org.tensorflow.op.core.Gradients}
   */
  public Gradients gradients(Operand y, Iterable> x, Gradients.Options... options) {
    return Gradients.create(scope, y, x, options);
  }

  /**
   * Adds an {@link ApplyGradientDescent} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param alpha Scaling factor. Must be a scalar.
   * @param delta The change.
   * @param options carries optional attributes values
   * @return a new instance of ApplyGradientDescent
   * @see {@link org.tensorflow.op.core.ApplyGradientDescent}
   */
  public  ApplyGradientDescent applyGradientDescent(Operand var, Operand alpha,
      Operand delta, ApplyGradientDescent.Options... options) {
    return ApplyGradientDescent.create(scope, var, alpha, delta, options);
  }

  /**
   * Adds an {@link UnravelIndex} operation to the graph
   *
   * @param indices An 0-D or 1-D `int` Tensor whose elements are indices into the
   * @param dims An 1-D `int` Tensor. The shape of the array to use for unraveling
   * @return a new instance of UnravelIndex
   * @see {@link org.tensorflow.op.core.UnravelIndex}
   */
  public  UnravelIndex unravelIndex(Operand indices, Operand dims) {
    return UnravelIndex.create(scope, indices, dims);
  }

  /**
   * Adds an {@link Greater} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of Greater
   * @see {@link org.tensorflow.op.core.Greater}
   */
  public  Greater greater(Operand x, Operand y) {
    return Greater.create(scope, x, y);
  }

  /**
   * Adds an {@link FixedLengthRecordDataset} operation to the graph
   *
   * @param filenames A scalar or a vector containing the name(s) of the file(s) to be
   * @param headerBytes A scalar representing the number of bytes to skip at the
   * @param recordBytes A scalar representing the number of bytes in each record.
   * @param footerBytes A scalar representing the number of bytes to skip at the end
   * @param bufferSize A scalar representing the number of bytes to buffer. Must be > 0.
   * @return a new instance of FixedLengthRecordDataset
   * @see {@link org.tensorflow.op.core.FixedLengthRecordDataset}
   */
  public FixedLengthRecordDataset fixedLengthRecordDataset(Operand filenames,
      Operand headerBytes, Operand recordBytes, Operand footerBytes,
      Operand bufferSize) {
    return FixedLengthRecordDataset.create(scope, filenames, headerBytes, recordBytes, footerBytes, bufferSize);
  }

  /**
   * Adds an {@link RandomPoissonV2} operation to the graph
   *
   * @param shape 1-D integer tensor. Shape of independent samples to draw from each
   * @param rate A tensor in which each scalar is a "rate" parameter describing the
   * @param dtype 
   * @param options carries optional attributes values
   * @return a new instance of RandomPoissonV2
   * @see {@link org.tensorflow.op.core.RandomPoissonV2}
   */
  public  RandomPoissonV2 randomPoissonV2(Operand shape,
      Operand rate, Class dtype, RandomPoissonV2.Options... options) {
    return RandomPoissonV2.create(scope, shape, rate, dtype, options);
  }

  /**
   * Adds an {@link Relu6} operation to the graph
   *
   * @param features 
   * @return a new instance of Relu6
   * @see {@link org.tensorflow.op.core.Relu6}
   */
  public  Relu6 relu6(Operand features) {
    return Relu6.create(scope, features);
  }

  /**
   * Adds an {@link FIFOQueue} operation to the graph
   *
   * @param componentTypes The type of each component in a value.
   * @param options carries optional attributes values
   * @return a new instance of FIFOQueue
   * @see {@link org.tensorflow.op.core.FIFOQueue}
   */
  public FIFOQueue fIFOQueue(List> componentTypes, FIFOQueue.Options... options) {
    return FIFOQueue.create(scope, componentTypes, options);
  }

  /**
   * Adds an {@link Conv3DBackpropInput} operation to the graph
   *
   * @param input Shape `[batch, depth, rows, cols, in_channels]`.
   * @param filter Shape `[depth, rows, cols, in_channels, out_channels]`.
   * @param outBackprop Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
   * @param strides 1-D tensor of length 5. The stride of the sliding window for each
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of Conv3DBackpropInput
   * @see {@link org.tensorflow.op.core.Conv3DBackpropInput}
   */
  public  Conv3DBackpropInput conv3DBackpropInput(Operand input,
      Operand filter, Operand outBackprop, List strides, String padding,
      Conv3DBackpropInput.Options... options) {
    return Conv3DBackpropInput.create(scope, input, filter, outBackprop, strides, padding, options);
  }

  /**
   * Adds an {@link PlaceholderWithDefault} operation to the graph
   *
   * @param input The default value to produce when `output` is not fed.
   * @param shape The (possibly partial) shape of the tensor.
   * @return a new instance of PlaceholderWithDefault
   * @see {@link org.tensorflow.op.core.PlaceholderWithDefault}
   */
  public  PlaceholderWithDefault placeholderWithDefault(Operand input, Shape shape) {
    return PlaceholderWithDefault.create(scope, input, shape);
  }

  /**
   * Adds an {@link MaxPoolGradV2} operation to the graph
   *
   * @param origInput The original input tensor.
   * @param origOutput The original output tensor.
   * @param grad 4-D.  Gradients w.r.t. the output of `max_pool`.
   * @param ksize The size of the window for each dimension of the input tensor.
   * @param strides The stride of the sliding window for each dimension of the
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of MaxPoolGradV2
   * @see {@link org.tensorflow.op.core.MaxPoolGradV2}
   */
  public  MaxPoolGradV2 maxPoolGradV2(Operand origInput,
      Operand origOutput, Operand grad, Operand ksize, Operand strides,
      String padding, MaxPoolGradV2.Options... options) {
    return MaxPoolGradV2.create(scope, origInput, origOutput, grad, ksize, strides, padding, options);
  }

  /**
   * Adds an {@link CudnnRNNParamsToCanonical} operation to the graph
   *
   * @param numLayers 
   * @param numUnits 
   * @param inputSize 
   * @param params 
   * @param numParams 
   * @param options carries optional attributes values
   * @return a new instance of CudnnRNNParamsToCanonical
   * @see {@link org.tensorflow.op.core.CudnnRNNParamsToCanonical}
   */
  public  CudnnRNNParamsToCanonical cudnnRNNParamsToCanonical(Operand numLayers,
      Operand numUnits, Operand inputSize, Operand params, Long numParams,
      CudnnRNNParamsToCanonical.Options... options) {
    return CudnnRNNParamsToCanonical.create(scope, numLayers, numUnits, inputSize, params, numParams, options);
  }

  /**
   * Adds an {@link OrderedMapClear} operation to the graph
   *
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of OrderedMapClear
   * @see {@link org.tensorflow.op.core.OrderedMapClear}
   */
  public OrderedMapClear orderedMapClear(List> dtypes,
      OrderedMapClear.Options... options) {
    return OrderedMapClear.create(scope, dtypes, options);
  }

  /**
   * Adds an {@link BatchMatrixSolve} operation to the graph
   *
   * @param matrix 
   * @param rhs 
   * @param options carries optional attributes values
   * @return a new instance of BatchMatrixSolve
   * @see {@link org.tensorflow.op.core.BatchMatrixSolve}
   */
  public  BatchMatrixSolve batchMatrixSolve(Operand matrix, Operand rhs,
      BatchMatrixSolve.Options... options) {
    return BatchMatrixSolve.create(scope, matrix, rhs, options);
  }

  /**
   * Adds an {@link ParseTensor} operation to the graph
   *
   * @param serialized A scalar string containing a serialized TensorProto proto.
   * @param outType The type of the serialized tensor.  The provided type must match the
   * @return a new instance of ParseTensor
   * @see {@link org.tensorflow.op.core.ParseTensor}
   */
  public  ParseTensor parseTensor(Operand serialized, Class outType) {
    return ParseTensor.create(scope, serialized, outType);
  }

  /**
   * Adds an {@link IsInf} operation to the graph
   *
   * @param x 
   * @return a new instance of IsInf
   * @see {@link org.tensorflow.op.core.IsInf}
   */
  public  IsInf isInf(Operand x) {
    return IsInf.create(scope, x);
  }

  /**
   * Adds an {@link ApplyAddSign} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param m Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param alpha Must be a scalar.
   * @param signDecay Must be a scalar.
   * @param beta Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ApplyAddSign
   * @see {@link org.tensorflow.op.core.ApplyAddSign}
   */
  public  ApplyAddSign applyAddSign(Operand var, Operand m, Operand lr,
      Operand alpha, Operand signDecay, Operand beta, Operand grad,
      ApplyAddSign.Options... options) {
    return ApplyAddSign.create(scope, var, m, lr, alpha, signDecay, beta, grad, options);
  }

  /**
   * Adds an {@link ResourceApplyMomentum} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param grad The gradient.
   * @param momentum Momentum. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of ResourceApplyMomentum
   * @see {@link org.tensorflow.op.core.ResourceApplyMomentum}
   */
  public  ResourceApplyMomentum resourceApplyMomentum(Operand var, Operand accum,
      Operand lr, Operand grad, Operand momentum,
      ResourceApplyMomentum.Options... options) {
    return ResourceApplyMomentum.create(scope, var, accum, lr, grad, momentum, options);
  }

  /**
   * Adds an {@link Stage} operation to the graph
   *
   * @param values a list of tensors
   * @param options carries optional attributes values
   * @return a new instance of Stage
   * @see {@link org.tensorflow.op.core.Stage}
   */
  public Stage stage(Iterable> values, Stage.Options... options) {
    return Stage.create(scope, values, options);
  }

  /**
   * Adds an {@link ConsumeMutexLock} operation to the graph
   *
   * @param mutexLock A tensor returned by `MutexLock`.
   * @return a new instance of ConsumeMutexLock
   * @see {@link org.tensorflow.op.core.ConsumeMutexLock}
   */
  public ConsumeMutexLock consumeMutexLock(Operand mutexLock) {
    return ConsumeMutexLock.create(scope, mutexLock);
  }

  /**
   * Adds an {@link StatsAggregatorHandle} operation to the graph
   *
   * @param options carries optional attributes values
   * @return a new instance of StatsAggregatorHandle
   * @see {@link org.tensorflow.op.core.StatsAggregatorHandle}
   */
  public StatsAggregatorHandle statsAggregatorHandle(StatsAggregatorHandle.Options... options) {
    return StatsAggregatorHandle.create(scope, options);
  }

  /**
   * Adds an {@link IsVariableInitialized} operation to the graph
   *
   * @param ref Should be from a `Variable` node. May be uninitialized.
   * @return a new instance of IsVariableInitialized
   * @see {@link org.tensorflow.op.core.IsVariableInitialized}
   */
  public  IsVariableInitialized isVariableInitialized(Operand ref) {
    return IsVariableInitialized.create(scope, ref);
  }

  /**
   * Adds an {@link ResourceScatterMul} operation to the graph
   *
   * @param resource Should be from a `Variable` node.
   * @param indices A tensor of indices into the first dimension of `ref`.
   * @param updates A tensor of updated values to add to `ref`.
   * @return a new instance of ResourceScatterMul
   * @see {@link org.tensorflow.op.core.ResourceScatterMul}
   */
  public  ResourceScatterMul resourceScatterMul(Operand resource,
      Operand indices, Operand updates) {
    return ResourceScatterMul.create(scope, resource, indices, updates);
  }

  /**
   * Adds an {@link MutableHashTableOfTensors} operation to the graph
   *
   * @param keyDtype Type of the table keys.
   * @param valueDtype Type of the table values.
   * @param options carries optional attributes values
   * @return a new instance of MutableHashTableOfTensors
   * @see {@link org.tensorflow.op.core.MutableHashTableOfTensors}
   */
  public  MutableHashTableOfTensors mutableHashTableOfTensors(Class keyDtype,
      Class valueDtype, MutableHashTableOfTensors.Options... options) {
    return MutableHashTableOfTensors.create(scope, keyDtype, valueDtype, options);
  }

  /**
   * Adds an {@link Conv2DBackpropInput} operation to the graph
   *
   * @param inputSizes An integer vector representing the shape of `input`,
   * @param filter 4-D with shape
   * @param outBackprop 4-D with shape `[batch, out_height, out_width, out_channels]`.
   * @param strides The stride of the sliding window for each dimension of the input
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of Conv2DBackpropInput
   * @see {@link org.tensorflow.op.core.Conv2DBackpropInput}
   */
  public  Conv2DBackpropInput conv2DBackpropInput(Operand inputSizes,
      Operand filter, Operand outBackprop, List strides, String padding,
      Conv2DBackpropInput.Options... options) {
    return Conv2DBackpropInput.create(scope, inputSizes, filter, outBackprop, strides, padding, options);
  }

  /**
   * Adds an {@link SparseFillEmptyRowsGrad} operation to the graph
   *
   * @param reverseIndexMap 1-D.  The reverse index map from SparseFillEmptyRows.
   * @param gradValues 1-D.  The gradients from backprop.
   * @return a new instance of SparseFillEmptyRowsGrad
   * @see {@link org.tensorflow.op.core.SparseFillEmptyRowsGrad}
   */
  public  SparseFillEmptyRowsGrad sparseFillEmptyRowsGrad(Operand reverseIndexMap,
      Operand gradValues) {
    return SparseFillEmptyRowsGrad.create(scope, reverseIndexMap, gradValues);
  }

  /**
   * Adds an {@link SparseReduceMaxSparse} operation to the graph
   *
   * @param inputIndices 2-D.  `N x R` matrix with the indices of non-empty values in a
   * @param inputValues 1-D.  `N` non-empty values corresponding to `input_indices`.
   * @param inputShape 1-D.  Shape of the input SparseTensor.
   * @param reductionAxes 1-D.  Length-`K` vector containing the reduction axes.
   * @param options carries optional attributes values
   * @return a new instance of SparseReduceMaxSparse
   * @see {@link org.tensorflow.op.core.SparseReduceMaxSparse}
   */
  public  SparseReduceMaxSparse sparseReduceMaxSparse(Operand inputIndices,
      Operand inputValues, Operand inputShape, Operand reductionAxes,
      SparseReduceMaxSparse.Options... options) {
    return SparseReduceMaxSparse.create(scope, inputIndices, inputValues, inputShape, reductionAxes, options);
  }

  /**
   * Adds an {@link Cosh} operation to the graph
   *
   * @param x 
   * @return a new instance of Cosh
   * @see {@link org.tensorflow.op.core.Cosh}
   */
  public  Cosh cosh(Operand x) {
    return Cosh.create(scope, x);
  }

  /**
   * Adds an {@link TensorArraySplit} operation to the graph
   *
   * @param handle The handle to a TensorArray.
   * @param value The concatenated tensor to write to the TensorArray.
   * @param lengths The vector of lengths, how to split the rows of value into the
   * @param flowIn A float scalar that enforces proper chaining of operations.
   * @return a new instance of TensorArraySplit
   * @see {@link org.tensorflow.op.core.TensorArraySplit}
   */
  public  TensorArraySplit tensorArraySplit(Operand handle, Operand value,
      Operand lengths, Operand flowIn) {
    return TensorArraySplit.create(scope, handle, value, lengths, flowIn);
  }

  /**
   * Adds an {@link UniformCandidateSampler} operation to the graph
   *
   * @param trueClasses A batch_size * num_true matrix, in which each row contains the
   * @param numTrue Number of true labels per context.
   * @param numSampled Number of candidates to randomly sample.
   * @param unique If unique is true, we sample with rejection, so that all sampled
   * @param rangeMax The sampler will sample integers from the interval [0, range_max).
   * @param options carries optional attributes values
   * @return a new instance of UniformCandidateSampler
   * @see {@link org.tensorflow.op.core.UniformCandidateSampler}
   */
  public UniformCandidateSampler uniformCandidateSampler(Operand trueClasses, Long numTrue,
      Long numSampled, Boolean unique, Long rangeMax, UniformCandidateSampler.Options... options) {
    return UniformCandidateSampler.create(scope, trueClasses, numTrue, numSampled, unique, rangeMax, options);
  }

  /**
   * Adds an {@link MatrixSetDiag} operation to the graph
   *
   * @param input Rank `k+1`, where `k >= 1`.
   * @param diagonal Rank `k`, where `k >= 1`.
   * @return a new instance of MatrixSetDiag
   * @see {@link org.tensorflow.op.core.MatrixSetDiag}
   */
  public  MatrixSetDiag matrixSetDiag(Operand input, Operand diagonal) {
    return MatrixSetDiag.create(scope, input, diagonal);
  }

  /**
   * Adds an {@link SerializeIterator} operation to the graph
   *
   * @param resourceHandle A handle to an iterator resource.
   * @return a new instance of SerializeIterator
   * @see {@link org.tensorflow.op.core.SerializeIterator}
   */
  public SerializeIterator serializeIterator(Operand resourceHandle) {
    return SerializeIterator.create(scope, resourceHandle);
  }

  /**
   * Adds an {@link AudioSpectrogram} operation to the graph
   *
   * @param input Float representation of audio data.
   * @param windowSize How wide the input window is in samples. For the highest efficiency
   * @param stride How widely apart the center of adjacent sample windows should be.
   * @param options carries optional attributes values
   * @return a new instance of AudioSpectrogram
   * @see {@link org.tensorflow.op.core.AudioSpectrogram}
   */
  public AudioSpectrogram audioSpectrogram(Operand input, Long windowSize, Long stride,
      AudioSpectrogram.Options... options) {
    return AudioSpectrogram.create(scope, input, windowSize, stride, options);
  }

  /**
   * Adds an {@link AccumulatorSetGlobalStep} operation to the graph
   *
   * @param handle The handle to an accumulator.
   * @param newGlobalStep The new global_step value to set.
   * @return a new instance of AccumulatorSetGlobalStep
   * @see {@link org.tensorflow.op.core.AccumulatorSetGlobalStep}
   */
  public AccumulatorSetGlobalStep accumulatorSetGlobalStep(Operand handle,
      Operand newGlobalStep) {
    return AccumulatorSetGlobalStep.create(scope, handle, newGlobalStep);
  }

  /**
   * Adds an {@link TensorArrayGradWithShape} operation to the graph
   *
   * @param handle The handle to the forward TensorArray.
   * @param flowIn A float scalar that enforces proper chaining of operations.
   * @param shapeToPrepend An int32 vector representing a shape. Elements in the gradient accumulator will
   * @param source The gradient source string, used to decide which gradient TensorArray
   * @return a new instance of TensorArrayGradWithShape
   * @see {@link org.tensorflow.op.core.TensorArrayGradWithShape}
   */
  public TensorArrayGradWithShape tensorArrayGradWithShape(Operand handle, Operand flowIn,
      Operand shapeToPrepend, String source) {
    return TensorArrayGradWithShape.create(scope, handle, flowIn, shapeToPrepend, source);
  }

  /**
   * Adds an {@link StringSplit} operation to the graph
   *
   * @param input 1-D. Strings to split.
   * @param delimiter 0-D. Delimiter characters (bytes), or empty string.
   * @param options carries optional attributes values
   * @return a new instance of StringSplit
   * @see {@link org.tensorflow.op.core.StringSplit}
   */
  public StringSplit stringSplit(Operand input, Operand delimiter,
      StringSplit.Options... options) {
    return StringSplit.create(scope, input, delimiter, options);
  }

  /**
   * Adds an {@link ResourceSparseApplyProximalAdagrad} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param lr Learning rate. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 regularization. Must be a scalar.
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param options carries optional attributes values
   * @return a new instance of ResourceSparseApplyProximalAdagrad
   * @see {@link org.tensorflow.op.core.ResourceSparseApplyProximalAdagrad}
   */
  public  ResourceSparseApplyProximalAdagrad resourceSparseApplyProximalAdagrad(Operand var,
      Operand accum, Operand lr, Operand l1, Operand l2, Operand grad,
      Operand indices, ResourceSparseApplyProximalAdagrad.Options... options) {
    return ResourceSparseApplyProximalAdagrad.create(scope, var, accum, lr, l1, l2, grad, indices, options);
  }

  /**
   * Adds an {@link ReaderNumWorkUnitsCompleted} operation to the graph
   *
   * @param readerHandle Handle to a Reader.
   * @return a new instance of ReaderNumWorkUnitsCompleted
   * @see {@link org.tensorflow.op.core.ReaderNumWorkUnitsCompleted}
   */
  public ReaderNumWorkUnitsCompleted readerNumWorkUnitsCompleted(Operand readerHandle) {
    return ReaderNumWorkUnitsCompleted.create(scope, readerHandle);
  }

  /**
   * Adds an {@link PadV2} operation to the graph
   *
   * @param input 
   * @param paddings 
   * @param constantValues 
   * @return a new instance of PadV2
   * @see {@link org.tensorflow.op.core.PadV2}
   */
  public  PadV2 padV2(Operand input, Operand paddings,
      Operand constantValues) {
    return PadV2.create(scope, input, paddings, constantValues);
  }

  /**
   * Adds an {@link ResourceScatterDiv} operation to the graph
   *
   * @param resource Should be from a `Variable` node.
   * @param indices A tensor of indices into the first dimension of `ref`.
   * @param updates A tensor of updated values to add to `ref`.
   * @return a new instance of ResourceScatterDiv
   * @see {@link org.tensorflow.op.core.ResourceScatterDiv}
   */
  public  ResourceScatterDiv resourceScatterDiv(Operand resource,
      Operand indices, Operand updates) {
    return ResourceScatterDiv.create(scope, resource, indices, updates);
  }

  /**
   * Adds an {@link SparseSegmentSum} operation to the graph
   *
   * @param data 
   * @param indices A 1-D tensor. Has same rank as `segment_ids`.
   * @param segmentIds A 1-D tensor. Values should be sorted and can be repeated.
   * @return a new instance of SparseSegmentSum
   * @see {@link org.tensorflow.op.core.SparseSegmentSum}
   */
  public  SparseSegmentSum sparseSegmentSum(Operand data,
      Operand indices, Operand segmentIds) {
    return SparseSegmentSum.create(scope, data, indices, segmentIds);
  }

  /**
   * Adds an {@link Sum} operation to the graph
   *
   * @param input The tensor to reduce.
   * @param axis The dimensions to reduce. Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of Sum
   * @see {@link org.tensorflow.op.core.Sum}
   */
  public  Sum sum(Operand input, Operand axis,
      Sum.Options... options) {
    return Sum.create(scope, input, axis, options);
  }

  /**
   * Adds an {@link ParseSingleExample} operation to the graph
   *
   * @param serialized A vector containing a batch of binary serialized Example protos.
   * @param denseDefaults A list of Tensors (some may be empty), whose length matches
   * @param numSparse The number of sparse features to be parsed from the example. This
   * @param sparseKeys A list of `num_sparse` strings.
   * @param denseKeys The keys expected in the Examples' features associated with dense
   * @param sparseTypes A list of `num_sparse` types; the data types of data in each
   * @param denseShapes The shapes of data in each Feature given in dense_keys.
   * @return a new instance of ParseSingleExample
   * @see {@link org.tensorflow.op.core.ParseSingleExample}
   */
  public ParseSingleExample parseSingleExample(Operand serialized,
      Iterable> denseDefaults, Long numSparse, List sparseKeys,
      List denseKeys, List> sparseTypes, List denseShapes) {
    return ParseSingleExample.create(scope, serialized, denseDefaults, numSparse, sparseKeys, denseKeys, sparseTypes, denseShapes);
  }

  /**
   * Adds an {@link Expm1} operation to the graph
   *
   * @param x 
   * @return a new instance of Expm1
   * @see {@link org.tensorflow.op.core.Expm1}
   */
  public  Expm1 expm1(Operand x) {
    return Expm1.create(scope, x);
  }

  /**
   * Adds an {@link SparseSlice} operation to the graph
   *
   * @param indices 2-D tensor represents the indices of the sparse tensor.
   * @param values 1-D tensor represents the values of the sparse tensor.
   * @param shape 1-D. tensor represents the shape of the sparse tensor.
   * @param start 1-D. tensor represents the start of the slice.
   * @param size 1-D. tensor represents the size of the slice.
   * @return a new instance of SparseSlice
   * @see {@link org.tensorflow.op.core.SparseSlice}
   */
  public  SparseSlice sparseSlice(Operand indices, Operand values,
      Operand shape, Operand start, Operand size) {
    return SparseSlice.create(scope, indices, values, shape, start, size);
  }

  /**
   * Adds an {@link InplaceAdd} operation to the graph
   *
   * @param x A `Tensor` of type T.
   * @param i A vector. Indices into the left-most dimension of `x`.
   * @param v A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size.
   * @return a new instance of InplaceAdd
   * @see {@link org.tensorflow.op.core.InplaceAdd}
   */
  public  InplaceAdd inplaceAdd(Operand x, Operand i, Operand v) {
    return InplaceAdd.create(scope, x, i, v);
  }

  /**
   * Adds an {@link StagePeek} operation to the graph
   *
   * @param index 
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of StagePeek
   * @see {@link org.tensorflow.op.core.StagePeek}
   */
  public StagePeek stagePeek(Operand index, List> dtypes,
      StagePeek.Options... options) {
    return StagePeek.create(scope, index, dtypes, options);
  }

  /**
   * Adds an {@link CTCBeamSearchDecoder} operation to the graph
   *
   * @param inputs 3-D, shape: `(max_time x batch_size x num_classes)`, the logits.
   * @param sequenceLength A vector containing sequence lengths, size `(batch)`.
   * @param beamWidth A scalar >= 0 (beam search beam width).
   * @param topPaths A scalar >= 0, <= beam_width (controls output size).
   * @param options carries optional attributes values
   * @return a new instance of CTCBeamSearchDecoder
   * @see {@link org.tensorflow.op.core.CTCBeamSearchDecoder}
   */
  public CTCBeamSearchDecoder cTCBeamSearchDecoder(Operand inputs,
      Operand sequenceLength, Long beamWidth, Long topPaths,
      CTCBeamSearchDecoder.Options... options) {
    return CTCBeamSearchDecoder.create(scope, inputs, sequenceLength, beamWidth, topPaths, options);
  }

  /**
   * Adds an {@link RFFT3D} operation to the graph
   *
   * @param input A float32 tensor.
   * @param fftLength An int32 tensor of shape [3]. The FFT length for each dimension.
   * @return a new instance of RFFT3D
   * @see {@link org.tensorflow.op.core.RFFT3D}
   */
  public RFFT3D rFFT3D(Operand input, Operand fftLength) {
    return RFFT3D.create(scope, input, fftLength);
  }

  /**
   * Adds an {@link ResourceApplyProximalGradientDescent} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param alpha Scaling factor. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 regularization. Must be a scalar.
   * @param delta The change.
   * @param options carries optional attributes values
   * @return a new instance of ResourceApplyProximalGradientDescent
   * @see {@link org.tensorflow.op.core.ResourceApplyProximalGradientDescent}
   */
  public  ResourceApplyProximalGradientDescent resourceApplyProximalGradientDescent(Operand var,
      Operand alpha, Operand l1, Operand l2, Operand delta,
      ResourceApplyProximalGradientDescent.Options... options) {
    return ResourceApplyProximalGradientDescent.create(scope, var, alpha, l1, l2, delta, options);
  }

  /**
   * Adds an {@link Save} operation to the graph
   *
   * @param filename Must have a single element. The name of the file to which we write
   * @param tensorNames Shape `[N]`. The names of the tensors to be saved.
   * @param data `N` tensors to save.
   * @return a new instance of Save
   * @see {@link org.tensorflow.op.core.Save}
   */
  public Save save(Operand filename, Operand tensorNames,
      Iterable> data) {
    return Save.create(scope, filename, tensorNames, data);
  }

  /**
   * Adds an {@link Bucketize} operation to the graph
   *
   * @param input Any shape of Tensor contains with int or float type.
   * @param boundaries A sorted list of floats gives the boundary of the buckets.
   * @return a new instance of Bucketize
   * @see {@link org.tensorflow.op.core.Bucketize}
   */
  public  Bucketize bucketize(Operand input, List boundaries) {
    return Bucketize.create(scope, input, boundaries);
  }

  /**
   * Adds an {@link ReduceJoin} operation to the graph
   *
   * @param inputs The input to be joined.  All reduced indices must have non-zero size.
   * @param reductionIndices The dimensions to reduce over.  Dimensions are reduced in the
   * @param options carries optional attributes values
   * @return a new instance of ReduceJoin
   * @see {@link org.tensorflow.op.core.ReduceJoin}
   */
  public ReduceJoin reduceJoin(Operand inputs, Operand reductionIndices,
      ReduceJoin.Options... options) {
    return ReduceJoin.create(scope, inputs, reductionIndices, options);
  }

  /**
   * Adds an {@link SparseTensorDenseAdd} operation to the graph
   *
   * @param aIndices 2-D.  The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`.
   * @param aValues 1-D.  The `values` of the `SparseTensor`, with shape `[nnz]`.
   * @param aShape 1-D.  The `shape` of the `SparseTensor`, with shape `[ndims]`.
   * @param b `ndims`-D Tensor.  With shape `a_shape`.
   * @return a new instance of SparseTensorDenseAdd
   * @see {@link org.tensorflow.op.core.SparseTensorDenseAdd}
   */
  public  SparseTensorDenseAdd sparseTensorDenseAdd(Operand aIndices,
      Operand aValues, Operand aShape, Operand b) {
    return SparseTensorDenseAdd.create(scope, aIndices, aValues, aShape, b);
  }

  /**
   * Adds an {@link SpaceToBatchND} operation to the graph
   *
   * @param input N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,
   * @param blockShape 1-D with shape `[M]`, all values must be >= 1.
   * @param paddings 2-D with shape `[M, 2]`, all values must be >= 0.
   * @return a new instance of SpaceToBatchND
   * @see {@link org.tensorflow.op.core.SpaceToBatchND}
   */
  public  SpaceToBatchND spaceToBatchND(Operand input,
      Operand blockShape, Operand paddings) {
    return SpaceToBatchND.create(scope, input, blockShape, paddings);
  }

  /**
   * Adds an {@link Unique} operation to the graph
   *
   * @param x 1-D.
   * @param outIdx 
   * @return a new instance of Unique
   * @see {@link org.tensorflow.op.core.Unique}
   */
  public  Unique unique(Operand x, Class outIdx) {
    return Unique.create(scope, x, outIdx);
  }

  /**
   * Adds an {@link Log} operation to the graph
   *
   * @param x 
   * @return a new instance of Log
   * @see {@link org.tensorflow.op.core.Log}
   */
  public  Log log(Operand x) {
    return Log.create(scope, x);
  }

  /**
   * Adds an {@link ComputeAccidentalHits} operation to the graph
   *
   * @param trueClasses The true_classes output of UnpackSparseLabels.
   * @param sampledCandidates The sampled_candidates output of CandidateSampler.
   * @param numTrue Number of true labels per context.
   * @param options carries optional attributes values
   * @return a new instance of ComputeAccidentalHits
   * @see {@link org.tensorflow.op.core.ComputeAccidentalHits}
   */
  public ComputeAccidentalHits computeAccidentalHits(Operand trueClasses,
      Operand sampledCandidates, Long numTrue, ComputeAccidentalHits.Options... options) {
    return ComputeAccidentalHits.create(scope, trueClasses, sampledCandidates, numTrue, options);
  }

  /**
   * Adds an {@link TensorListFromTensor} operation to the graph
   *
   * @param tensor 
   * @param elementShape 
   * @return a new instance of TensorListFromTensor
   * @see {@link org.tensorflow.op.core.TensorListFromTensor}
   */
  public  TensorListFromTensor tensorListFromTensor(Operand tensor,
      Operand elementShape) {
    return TensorListFromTensor.create(scope, tensor, elementShape);
  }

  /**
   * Adds an {@link Softplus} operation to the graph
   *
   * @param features 
   * @return a new instance of Softplus
   * @see {@link org.tensorflow.op.core.Softplus}
   */
  public  Softplus softplus(Operand features) {
    return Softplus.create(scope, features);
  }

  /**
   * Adds an {@link GuaranteeConst} operation to the graph
   *
   * @param input 
   * @return a new instance of GuaranteeConst
   * @see {@link org.tensorflow.op.core.GuaranteeConst}
   */
  public  GuaranteeConst guaranteeConst(Operand input) {
    return GuaranteeConst.create(scope, input);
  }

  /**
   * Adds an {@link Abort} operation to the graph
   *
   * @param options carries optional attributes values
   * @return a new instance of Abort
   * @see {@link org.tensorflow.op.core.Abort}
   */
  public Abort abort(Abort.Options... options) {
    return Abort.create(scope, options);
  }

  /**
   * Adds an {@link BatchMatrixDeterminant} operation to the graph
   *
   * @param input 
   * @return a new instance of BatchMatrixDeterminant
   * @see {@link org.tensorflow.op.core.BatchMatrixDeterminant}
   */
  public  BatchMatrixDeterminant batchMatrixDeterminant(Operand input) {
    return BatchMatrixDeterminant.create(scope, input);
  }

  /**
   * Adds an {@link Angle} operation to the graph
   *
   * @param input 
   * @param Tout 
   * @return a new instance of Angle
   * @see {@link org.tensorflow.op.core.Angle}
   */
  public  Angle angle(Operand input, Class Tout) {
    return Angle.create(scope, input, Tout);
  }

  /**
   * Adds an {@link CountUpTo} operation to the graph
   *
   * @param ref Should be from a scalar `Variable` node.
   * @param limit If incrementing ref would bring it above limit, instead generates an
   * @return a new instance of CountUpTo
   * @see {@link org.tensorflow.op.core.CountUpTo}
   */
  public  CountUpTo countUpTo(Operand ref, Long limit) {
    return CountUpTo.create(scope, ref, limit);
  }

  /**
   * Adds an {@link TensorListPushBackBatch} operation to the graph
   *
   * @param inputHandles 
   * @param tensor 
   * @return a new instance of TensorListPushBackBatch
   * @see {@link org.tensorflow.op.core.TensorListPushBackBatch}
   */
  public  TensorListPushBackBatch tensorListPushBackBatch(Operand inputHandles,
      Operand tensor) {
    return TensorListPushBackBatch.create(scope, inputHandles, tensor);
  }

  /**
   * Adds an {@link RefNextIteration} operation to the graph
   *
   * @param data The tensor to be made available to the next iteration.
   * @return a new instance of RefNextIteration
   * @see {@link org.tensorflow.op.core.RefNextIteration}
   */
  public  RefNextIteration refNextIteration(Operand data) {
    return RefNextIteration.create(scope, data);
  }

  /**
   * Adds an {@link GetSessionHandleV2} operation to the graph
   *
   * @param value The tensor to be stored.
   * @return a new instance of GetSessionHandleV2
   * @see {@link org.tensorflow.op.core.GetSessionHandleV2}
   */
  public  GetSessionHandleV2 getSessionHandleV2(Operand value) {
    return GetSessionHandleV2.create(scope, value);
  }

  /**
   * Adds an {@link ResourceApplyFtrl} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param linear Should be from a Variable().
   * @param grad The gradient.
   * @param lr Scaling factor. Must be a scalar.
   * @param l1 L1 regulariation. Must be a scalar.
   * @param l2 L2 regulariation. Must be a scalar.
   * @param lrPower Scaling factor. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of ResourceApplyFtrl
   * @see {@link org.tensorflow.op.core.ResourceApplyFtrl}
   */
  public  ResourceApplyFtrl resourceApplyFtrl(Operand var, Operand accum,
      Operand linear, Operand grad, Operand lr, Operand l1, Operand l2,
      Operand lrPower, ResourceApplyFtrl.Options... options) {
    return ResourceApplyFtrl.create(scope, var, accum, linear, grad, lr, l1, l2, lrPower, options);
  }

  /**
   * Adds an {@link FakeQuantWithMinMaxVarsGradient} operation to the graph
   *
   * @param gradients Backpropagated gradients above the FakeQuantWithMinMaxVars operation.
   * @param inputs Values passed as inputs to the FakeQuantWithMinMaxVars operation.
   * @param min 
   * @param max 
   * @param options carries optional attributes values
   * @return a new instance of FakeQuantWithMinMaxVarsGradient
   * @see {@link org.tensorflow.op.core.FakeQuantWithMinMaxVarsGradient}
   */
  public FakeQuantWithMinMaxVarsGradient fakeQuantWithMinMaxVarsGradient(Operand gradients,
      Operand inputs, Operand min, Operand max,
      FakeQuantWithMinMaxVarsGradient.Options... options) {
    return FakeQuantWithMinMaxVarsGradient.create(scope, gradients, inputs, min, max, options);
  }

  /**
   * Adds an {@link DeserializeIterator} operation to the graph
   *
   * @param resourceHandle A handle to an iterator resource.
   * @param serialized A variant tensor storing the state of the iterator contained in the
   * @return a new instance of DeserializeIterator
   * @see {@link org.tensorflow.op.core.DeserializeIterator}
   */
  public DeserializeIterator deserializeIterator(Operand resourceHandle, Operand serialized) {
    return DeserializeIterator.create(scope, resourceHandle, serialized);
  }

  /**
   * Adds an {@link ReduceAny} operation to the graph
   *
   * @param input The tensor to reduce.
   * @param axis The dimensions to reduce. Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of ReduceAny
   * @see {@link org.tensorflow.op.core.ReduceAny}
   */
  public  ReduceAny reduceAny(Operand input, Operand axis,
      ReduceAny.Options... options) {
    return ReduceAny.create(scope, input, axis, options);
  }

  /**
   * Adds an {@link Identity} operation to the graph
   *
   * @param input 
   * @return a new instance of Identity
   * @see {@link org.tensorflow.op.core.Identity}
   */
  public  Identity identity(Operand input) {
    return Identity.create(scope, input);
  }

  /**
   * Adds an {@link PopulationCount} operation to the graph
   *
   * @param x 
   * @return a new instance of PopulationCount
   * @see {@link org.tensorflow.op.core.PopulationCount}
   */
  public  PopulationCount populationCount(Operand x) {
    return PopulationCount.create(scope, x);
  }

  /**
   * Adds an {@link RegexFullMatch} operation to the graph
   *
   * @param input A string tensor of the text to be processed.
   * @param pattern A 1-D string tensor of the regular expression to match the input.
   * @return a new instance of RegexFullMatch
   * @see {@link org.tensorflow.op.core.RegexFullMatch}
   */
  public RegexFullMatch regexFullMatch(Operand input, Operand pattern) {
    return RegexFullMatch.create(scope, input, pattern);
  }

  /**
   * Adds an {@link BesselI0e} operation to the graph
   *
   * @param x 
   * @return a new instance of BesselI0e
   * @see {@link org.tensorflow.op.core.BesselI0e}
   */
  public  BesselI0e besselI0e(Operand x) {
    return BesselI0e.create(scope, x);
  }

  /**
   * Adds an {@link QuantizedRelu} operation to the graph
   *
   * @param features 
   * @param minFeatures The float value that the lowest quantized value represents.
   * @param maxFeatures The float value that the highest quantized value represents.
   * @param outType 
   * @return a new instance of QuantizedRelu
   * @see {@link org.tensorflow.op.core.QuantizedRelu}
   */
  public  QuantizedRelu quantizedRelu(Operand features, Operand minFeatures,
      Operand maxFeatures, Class outType) {
    return QuantizedRelu.create(scope, features, minFeatures, maxFeatures, outType);
  }

  /**
   * Adds an {@link TensorListReserve} operation to the graph
   *
   * @param elementShape 
   * @param numElements 
   * @param elementDtype 
   * @return a new instance of TensorListReserve
   * @see {@link org.tensorflow.op.core.TensorListReserve}
   */
  public  TensorListReserve tensorListReserve(Operand elementShape,
      Operand numElements, Class elementDtype) {
    return TensorListReserve.create(scope, elementShape, numElements, elementDtype);
  }

  /**
   * Adds an {@link ResourceApplyProximalAdagrad} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 regularization. Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ResourceApplyProximalAdagrad
   * @see {@link org.tensorflow.op.core.ResourceApplyProximalAdagrad}
   */
  public  ResourceApplyProximalAdagrad resourceApplyProximalAdagrad(Operand var,
      Operand accum, Operand lr, Operand l1, Operand l2, Operand grad,
      ResourceApplyProximalAdagrad.Options... options) {
    return ResourceApplyProximalAdagrad.create(scope, var, accum, lr, l1, l2, grad, options);
  }

  /**
   * Adds an {@link Lgamma} operation to the graph
   *
   * @param x 
   * @return a new instance of Lgamma
   * @see {@link org.tensorflow.op.core.Lgamma}
   */
  public  Lgamma lgamma(Operand x) {
    return Lgamma.create(scope, x);
  }

  /**
   * Adds an {@link ReaderReset} operation to the graph
   *
   * @param readerHandle Handle to a Reader.
   * @return a new instance of ReaderReset
   * @see {@link org.tensorflow.op.core.ReaderReset}
   */
  public ReaderReset readerReset(Operand readerHandle) {
    return ReaderReset.create(scope, readerHandle);
  }

  /**
   * Adds an {@link LookupTableExport} operation to the graph
   *
   * @param tableHandle Handle to the table.
   * @param Tkeys 
   * @param Tvalues 
   * @return a new instance of LookupTableExport
   * @see {@link org.tensorflow.op.core.LookupTableExport}
   */
  public  LookupTableExport lookupTableExport(Operand tableHandle, Class Tkeys,
      Class Tvalues) {
    return LookupTableExport.create(scope, tableHandle, Tkeys, Tvalues);
  }

  /**
   * Adds an {@link FixedUnigramCandidateSampler} operation to the graph
   *
   * @param trueClasses A batch_size * num_true matrix, in which each row contains the
   * @param numTrue Number of true labels per context.
   * @param numSampled Number of candidates to randomly sample.
   * @param unique If unique is true, we sample with rejection, so that all sampled
   * @param rangeMax The sampler will sample integers from the interval [0, range_max).
   * @param options carries optional attributes values
   * @return a new instance of FixedUnigramCandidateSampler
   * @see {@link org.tensorflow.op.core.FixedUnigramCandidateSampler}
   */
  public FixedUnigramCandidateSampler fixedUnigramCandidateSampler(Operand trueClasses,
      Long numTrue, Long numSampled, Boolean unique, Long rangeMax,
      FixedUnigramCandidateSampler.Options... options) {
    return FixedUnigramCandidateSampler.create(scope, trueClasses, numTrue, numSampled, unique, rangeMax, options);
  }

  /**
   * Adds an {@link ScatterNdSub} operation to the graph
   *
   * @param ref A mutable Tensor. Should be from a Variable node.
   * @param indices A Tensor. Must be one of the following types: int32, int64.
   * @param updates A Tensor. Must have the same type as ref. A tensor of updated values
   * @param options carries optional attributes values
   * @return a new instance of ScatterNdSub
   * @see {@link org.tensorflow.op.core.ScatterNdSub}
   */
  public  ScatterNdSub scatterNdSub(Operand ref, Operand indices,
      Operand updates, ScatterNdSub.Options... options) {
    return ScatterNdSub.create(scope, ref, indices, updates, options);
  }

  /**
   * Adds an {@link BiasAddGrad} operation to the graph
   *
   * @param outBackprop Any number of dimensions.
   * @param options carries optional attributes values
   * @return a new instance of BiasAddGrad
   * @see {@link org.tensorflow.op.core.BiasAddGrad}
   */
  public  BiasAddGrad biasAddGrad(Operand outBackprop, BiasAddGrad.Options... options) {
    return BiasAddGrad.create(scope, outBackprop, options);
  }

  /**
   * Adds an {@link ResourceStridedSliceAssign} operation to the graph
   *
   * @param ref 
   * @param begin 
   * @param end 
   * @param strides 
   * @param value 
   * @param options carries optional attributes values
   * @return a new instance of ResourceStridedSliceAssign
   * @see {@link org.tensorflow.op.core.ResourceStridedSliceAssign}
   */
  public  ResourceStridedSliceAssign resourceStridedSliceAssign(Operand ref,
      Operand begin, Operand end, Operand strides, Operand value,
      ResourceStridedSliceAssign.Options... options) {
    return ResourceStridedSliceAssign.create(scope, ref, begin, end, strides, value, options);
  }

  /**
   * Adds an {@link ResourceApplyAdam} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param m Should be from a Variable().
   * @param v Should be from a Variable().
   * @param beta1Power Must be a scalar.
   * @param beta2Power Must be a scalar.
   * @param lr Scaling factor. Must be a scalar.
   * @param beta1 Momentum factor. Must be a scalar.
   * @param beta2 Momentum factor. Must be a scalar.
   * @param epsilon Ridge term. Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ResourceApplyAdam
   * @see {@link org.tensorflow.op.core.ResourceApplyAdam}
   */
  public  ResourceApplyAdam resourceApplyAdam(Operand var, Operand m, Operand v,
      Operand beta1Power, Operand beta2Power, Operand lr, Operand beta1,
      Operand beta2, Operand epsilon, Operand grad, ResourceApplyAdam.Options... options) {
    return ResourceApplyAdam.create(scope, var, m, v, beta1Power, beta2Power, lr, beta1, beta2, epsilon, grad, options);
  }

  /**
   * Adds an {@link BatchCholesky} operation to the graph
   *
   * @param input 
   * @return a new instance of BatchCholesky
   * @see {@link org.tensorflow.op.core.BatchCholesky}
   */
  public  BatchCholesky batchCholesky(Operand input) {
    return BatchCholesky.create(scope, input);
  }

  /**
   * Adds an {@link FeatureStatsDataset} operation to the graph
   *
   * @param inputDataset 
   * @param tag 
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of FeatureStatsDataset
   * @see {@link org.tensorflow.op.core.FeatureStatsDataset}
   */
  public FeatureStatsDataset featureStatsDataset(Operand inputDataset, Operand tag,
      List> outputTypes, List outputShapes) {
    return FeatureStatsDataset.create(scope, inputDataset, tag, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link MatrixSolve} operation to the graph
   *
   * @param matrix Shape is `[..., M, M]`.
   * @param rhs Shape is `[..., M, K]`.
   * @param options carries optional attributes values
   * @return a new instance of MatrixSolve
   * @see {@link org.tensorflow.op.core.MatrixSolve}
   */
  public  MatrixSolve matrixSolve(Operand matrix, Operand rhs,
      MatrixSolve.Options... options) {
    return MatrixSolve.create(scope, matrix, rhs, options);
  }

  /**
   * Adds an {@link DecodeCompressed} operation to the graph
   *
   * @param bytes A Tensor of string which is compressed.
   * @param options carries optional attributes values
   * @return a new instance of DecodeCompressed
   * @see {@link org.tensorflow.op.core.DecodeCompressed}
   */
  public DecodeCompressed decodeCompressed(Operand bytes,
      DecodeCompressed.Options... options) {
    return DecodeCompressed.create(scope, bytes, options);
  }

  /**
   * Adds an {@link SparseReshape} operation to the graph
   *
   * @param inputIndices 2-D.  `N x R_in` matrix with the indices of non-empty values in a
   * @param inputShape 1-D.  `R_in` vector with the input SparseTensor's dense shape.
   * @param newShape 1-D.  `R_out` vector with the requested new dense shape.
   * @return a new instance of SparseReshape
   * @see {@link org.tensorflow.op.core.SparseReshape}
   */
  public SparseReshape sparseReshape(Operand inputIndices, Operand inputShape,
      Operand newShape) {
    return SparseReshape.create(scope, inputIndices, inputShape, newShape);
  }

  /**
   * Adds an {@link ParseExample} operation to the graph
   *
   * @param serialized A vector containing a batch of binary serialized Example protos.
   * @param names A vector containing the names of the serialized protos.
   * @param sparseKeys A list of Nsparse string Tensors (scalars).
   * @param denseKeys A list of Ndense string Tensors (scalars).
   * @param denseDefaults A list of Ndense Tensors (some may be empty).
   * @param sparseTypes A list of Nsparse types; the data types of data in each Feature
   * @param denseShapes A list of Ndense shapes; the shapes of data in each Feature
   * @return a new instance of ParseExample
   * @see {@link org.tensorflow.op.core.ParseExample}
   */
  public ParseExample parseExample(Operand serialized, Operand names,
      Iterable> sparseKeys, Iterable> denseKeys,
      Iterable> denseDefaults, List> sparseTypes, List denseShapes) {
    return ParseExample.create(scope, serialized, names, sparseKeys, denseKeys, denseDefaults, sparseTypes, denseShapes);
  }

  /**
   * Adds an {@link SparseAdd} operation to the graph
   *
   * @param aIndices 2-D.  The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix.
   * @param aValues 1-D.  The `values` of the first `SparseTensor`, size `[nnz]` Vector.
   * @param aShape 1-D.  The `shape` of the first `SparseTensor`, size `[ndims]` Vector.
   * @param bIndices 2-D.  The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix.
   * @param bValues 1-D.  The `values` of the second `SparseTensor`, size `[nnz]` Vector.
   * @param bShape 1-D.  The `shape` of the second `SparseTensor`, size `[ndims]` Vector.
   * @param thresh 0-D.  The magnitude threshold that determines if an output value/index
   * @return a new instance of SparseAdd
   * @see {@link org.tensorflow.op.core.SparseAdd}
   */
  public  SparseAdd sparseAdd(Operand aIndices, Operand aValues,
      Operand aShape, Operand bIndices, Operand bValues, Operand bShape,
      Operand thresh) {
    return SparseAdd.create(scope, aIndices, aValues, aShape, bIndices, bValues, bShape, thresh);
  }

  /**
   * Adds an {@link SparseApplyAdadelta} operation to the graph
   *
   * @param var 
   * @param accum Should be from a Variable().
   * @param accumUpdate : Should be from a Variable().
   * @param lr Learning rate. Must be a scalar.
   * @param rho Decay factor. Must be a scalar.
   * @param epsilon Constant factor. Must be a scalar.
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param options carries optional attributes values
   * @return a new instance of SparseApplyAdadelta
   * @see {@link org.tensorflow.op.core.SparseApplyAdadelta}
   */
  public  SparseApplyAdadelta sparseApplyAdadelta(Operand var,
      Operand accum, Operand accumUpdate, Operand lr, Operand rho, Operand epsilon,
      Operand grad, Operand indices, SparseApplyAdadelta.Options... options) {
    return SparseApplyAdadelta.create(scope, var, accum, accumUpdate, lr, rho, epsilon, grad, indices, options);
  }

  /**
   * Adds an {@link SegmentProd} operation to the graph
   *
   * @param data 
   * @param segmentIds A 1-D tensor whose rank is equal to the rank of `data`'s
   * @return a new instance of SegmentProd
   * @see {@link org.tensorflow.op.core.SegmentProd}
   */
  public  SegmentProd segmentProd(Operand data, Operand segmentIds) {
    return SegmentProd.create(scope, data, segmentIds);
  }

  /**
   * Adds an {@link DepthwiseConv2dNativeBackpropFilter} operation to the graph
   *
   * @param input 4-D with shape based on `data_format`.  For example, if
   * @param filterSizes An integer vector representing the tensor shape of `filter`,
   * @param outBackprop 4-D with shape  based on `data_format`.
   * @param strides The stride of the sliding window for each dimension of the input
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of DepthwiseConv2dNativeBackpropFilter
   * @see {@link org.tensorflow.op.core.DepthwiseConv2dNativeBackpropFilter}
   */
  public  DepthwiseConv2dNativeBackpropFilter depthwiseConv2dNativeBackpropFilter(Operand input,
      Operand filterSizes, Operand outBackprop, List strides, String padding,
      DepthwiseConv2dNativeBackpropFilter.Options... options) {
    return DepthwiseConv2dNativeBackpropFilter.create(scope, input, filterSizes, outBackprop, strides, padding, options);
  }

  /**
   * Adds an {@link ExpandDims} operation to the graph
   *
   * @param input 
   * @param axis 0-D (scalar). Specifies the dimension index at which to
   * @return a new instance of ExpandDims
   * @see {@link org.tensorflow.op.core.ExpandDims}
   */
  public  ExpandDims expandDims(Operand input, Operand axis) {
    return ExpandDims.create(scope, input, axis);
  }

  /**
   * Adds an {@link TakeManySparseFromTensorsMap} operation to the graph
   *
   * @param sparseHandles 1-D, The `N` serialized `SparseTensor` objects.
   * @param dtype The `dtype` of the `SparseTensor` objects stored in the
   * @param options carries optional attributes values
   * @return a new instance of TakeManySparseFromTensorsMap
   * @see {@link org.tensorflow.op.core.TakeManySparseFromTensorsMap}
   */
  public  TakeManySparseFromTensorsMap takeManySparseFromTensorsMap(Operand sparseHandles,
      Class dtype, TakeManySparseFromTensorsMap.Options... options) {
    return TakeManySparseFromTensorsMap.create(scope, sparseHandles, dtype, options);
  }

  /**
   * Adds an {@link IFFT2D} operation to the graph
   *
   * @param input A complex64 tensor.
   * @return a new instance of IFFT2D
   * @see {@link org.tensorflow.op.core.IFFT2D}
   */
  public  IFFT2D iFFT2D(Operand input) {
    return IFFT2D.create(scope, input);
  }

  /**
   * Adds an {@link QueueDequeueUpTo} operation to the graph
   *
   * @param handle The handle to a queue.
   * @param n The number of tuples to dequeue.
   * @param componentTypes The type of each component in a tuple.
   * @param options carries optional attributes values
   * @return a new instance of QueueDequeueUpTo
   * @see {@link org.tensorflow.op.core.QueueDequeueUpTo}
   */
  public QueueDequeueUpTo queueDequeueUpTo(Operand handle, Operand n,
      List> componentTypes, QueueDequeueUpTo.Options... options) {
    return QueueDequeueUpTo.create(scope, handle, n, componentTypes, options);
  }

  /**
   * Adds an {@link OrderedMapPeek} operation to the graph
   *
   * @param key 
   * @param indices 
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of OrderedMapPeek
   * @see {@link org.tensorflow.op.core.OrderedMapPeek}
   */
  public OrderedMapPeek orderedMapPeek(Operand key, Operand indices,
      List> dtypes, OrderedMapPeek.Options... options) {
    return OrderedMapPeek.create(scope, key, indices, dtypes, options);
  }

  /**
   * Adds an {@link BatchSelfAdjointEigV2} operation to the graph
   *
   * @param input 
   * @param options carries optional attributes values
   * @return a new instance of BatchSelfAdjointEigV2
   * @see {@link org.tensorflow.op.core.BatchSelfAdjointEigV2}
   */
  public  BatchSelfAdjointEigV2 batchSelfAdjointEigV2(Operand input,
      BatchSelfAdjointEigV2.Options... options) {
    return BatchSelfAdjointEigV2.create(scope, input, options);
  }

  /**
   * Adds an {@link Cross} operation to the graph
   *
   * @param a A tensor containing 3-element vectors.
   * @param b Another tensor, of same type and shape as `a`.
   * @return a new instance of Cross
   * @see {@link org.tensorflow.op.core.Cross}
   */
  public  Cross cross(Operand a, Operand b) {
    return Cross.create(scope, a, b);
  }

  /**
   * Adds an {@link ScatterMul} operation to the graph
   *
   * @param ref Should be from a `Variable` node.
   * @param indices A tensor of indices into the first dimension of `ref`.
   * @param updates A tensor of updated values to multiply to `ref`.
   * @param options carries optional attributes values
   * @return a new instance of ScatterMul
   * @see {@link org.tensorflow.op.core.ScatterMul}
   */
  public  ScatterMul scatterMul(Operand ref, Operand indices,
      Operand updates, ScatterMul.Options... options) {
    return ScatterMul.create(scope, ref, indices, updates, options);
  }

  /**
   * Adds an {@link QuantizedRelu6} operation to the graph
   *
   * @param features 
   * @param minFeatures The float value that the lowest quantized value represents.
   * @param maxFeatures The float value that the highest quantized value represents.
   * @param outType 
   * @return a new instance of QuantizedRelu6
   * @see {@link org.tensorflow.op.core.QuantizedRelu6}
   */
  public  QuantizedRelu6 quantizedRelu6(Operand features, Operand minFeatures,
      Operand maxFeatures, Class outType) {
    return QuantizedRelu6.create(scope, features, minFeatures, maxFeatures, outType);
  }

  /**
   * Adds an {@link Exp} operation to the graph
   *
   * @param x 
   * @return a new instance of Exp
   * @see {@link org.tensorflow.op.core.Exp}
   */
  public  Exp exp(Operand x) {
    return Exp.create(scope, x);
  }

  /**
   * Adds an {@link ZipDataset} operation to the graph
   *
   * @param inputDatasets 
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of ZipDataset
   * @see {@link org.tensorflow.op.core.ZipDataset}
   */
  public ZipDataset zipDataset(Iterable> inputDatasets, List> outputTypes,
      List outputShapes) {
    return ZipDataset.create(scope, inputDatasets, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link LoopCond} operation to the graph
   *
   * @param input A boolean scalar, representing the branch predicate of the Switch op.
   * @return a new instance of LoopCond
   * @see {@link org.tensorflow.op.core.LoopCond}
   */
  public LoopCond loopCond(Operand input) {
    return LoopCond.create(scope, input);
  }

  /**
   * Adds an {@link ReduceProd} operation to the graph
   *
   * @param input The tensor to reduce.
   * @param axis The dimensions to reduce. Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of ReduceProd
   * @see {@link org.tensorflow.op.core.ReduceProd}
   */
  public  ReduceProd reduceProd(Operand input, Operand axis,
      ReduceProd.Options... options) {
    return ReduceProd.create(scope, input, axis, options);
  }

  /**
   * Adds an {@link EncodePng} operation to the graph
   *
   * @param image 3-D with shape `[height, width, channels]`.
   * @param options carries optional attributes values
   * @return a new instance of EncodePng
   * @see {@link org.tensorflow.op.core.EncodePng}
   */
  public  EncodePng encodePng(Operand image, EncodePng.Options... options) {
    return EncodePng.create(scope, image, options);
  }

  /**
   * Adds an {@link SparseSegmentMeanWithNumSegments} operation to the graph
   *
   * @param data 
   * @param indices A 1-D tensor. Has same rank as `segment_ids`.
   * @param segmentIds A 1-D tensor. Values should be sorted and can be repeated.
   * @param numSegments Should equal the number of distinct segment IDs.
   * @return a new instance of SparseSegmentMeanWithNumSegments
   * @see {@link org.tensorflow.op.core.SparseSegmentMeanWithNumSegments}
   */
  public  SparseSegmentMeanWithNumSegments sparseSegmentMeanWithNumSegments(Operand data,
      Operand indices, Operand segmentIds, Operand numSegments) {
    return SparseSegmentMeanWithNumSegments.create(scope, data, indices, segmentIds, numSegments);
  }

  /**
   * Adds an {@link BatchMatrixSolveLs} operation to the graph
   *
   * @param matrix 
   * @param rhs 
   * @param l2Regularizer 
   * @param options carries optional attributes values
   * @return a new instance of BatchMatrixSolveLs
   * @see {@link org.tensorflow.op.core.BatchMatrixSolveLs}
   */
  public  BatchMatrixSolveLs batchMatrixSolveLs(Operand matrix,
      Operand rhs, Operand l2Regularizer, BatchMatrixSolveLs.Options... options) {
    return BatchMatrixSolveLs.create(scope, matrix, rhs, l2Regularizer, options);
  }

  /**
   * Adds an {@link ParseSingleSequenceExample} operation to the graph
   *
   * @param serialized A scalar containing a binary serialized SequenceExample proto.
   * @param featureListDenseMissingAssumedEmpty A vector listing the
   * @param contextSparseKeys A list of Ncontext_sparse string Tensors (scalars).
   * @param contextDenseKeys A list of Ncontext_dense string Tensors (scalars).
   * @param featureListSparseKeys A list of Nfeature_list_sparse string Tensors
   * @param featureListDenseKeys A list of Nfeature_list_dense string Tensors (scalars).
   * @param contextDenseDefaults A list of Ncontext_dense Tensors (some may be empty).
   * @param debugName A scalar containing the name of the serialized proto.
   * @param contextSparseTypes A list of Ncontext_sparse types; the data types of data in
   * @param featureListDenseTypes 
   * @param featureListSparseTypes A list of Nfeature_list_sparse types; the data types
   * @param options carries optional attributes values
   * @return a new instance of ParseSingleSequenceExample
   * @see {@link org.tensorflow.op.core.ParseSingleSequenceExample}
   */
  public ParseSingleSequenceExample parseSingleSequenceExample(Operand serialized,
      Operand featureListDenseMissingAssumedEmpty,
      Iterable> contextSparseKeys, Iterable> contextDenseKeys,
      Iterable> featureListSparseKeys,
      Iterable> featureListDenseKeys, Iterable> contextDenseDefaults,
      Operand debugName, List> contextSparseTypes,
      List> featureListDenseTypes, List> featureListSparseTypes,
      ParseSingleSequenceExample.Options... options) {
    return ParseSingleSequenceExample.create(scope, serialized, featureListDenseMissingAssumedEmpty, contextSparseKeys, contextDenseKeys, featureListSparseKeys, featureListDenseKeys, contextDenseDefaults, debugName, contextSparseTypes, featureListDenseTypes, featureListSparseTypes, options);
  }

  /**
   * Adds an {@link ResourceApplyRMSProp} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param ms Should be from a Variable().
   * @param mom Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param rho Decay rate. Must be a scalar.
   * @param momentum 
   * @param epsilon Ridge term. Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ResourceApplyRMSProp
   * @see {@link org.tensorflow.op.core.ResourceApplyRMSProp}
   */
  public  ResourceApplyRMSProp resourceApplyRMSProp(Operand var, Operand ms,
      Operand mom, Operand lr, Operand rho, Operand momentum, Operand epsilon,
      Operand grad, ResourceApplyRMSProp.Options... options) {
    return ResourceApplyRMSProp.create(scope, var, ms, mom, lr, rho, momentum, epsilon, grad, options);
  }

  /**
   * Adds an {@link FakeQuantWithMinMaxVarsPerChannelGradient} operation to the graph
   *
   * @param gradients Backpropagated gradients above the FakeQuantWithMinMaxVars operation,
   * @param inputs Values passed as inputs to the FakeQuantWithMinMaxVars operation, shape
   * @param min 
   * @param max 
   * @param options carries optional attributes values
   * @return a new instance of FakeQuantWithMinMaxVarsPerChannelGradient
   * @see {@link org.tensorflow.op.core.FakeQuantWithMinMaxVarsPerChannelGradient}
   */
  public FakeQuantWithMinMaxVarsPerChannelGradient fakeQuantWithMinMaxVarsPerChannelGradient(Operand gradients,
      Operand inputs, Operand min, Operand max,
      FakeQuantWithMinMaxVarsPerChannelGradient.Options... options) {
    return FakeQuantWithMinMaxVarsPerChannelGradient.create(scope, gradients, inputs, min, max, options);
  }

  /**
   * Adds an {@link CompareAndBitpack} operation to the graph
   *
   * @param input Values to compare against `threshold` and bitpack.
   * @param threshold Threshold to compare against.
   * @return a new instance of CompareAndBitpack
   * @see {@link org.tensorflow.op.core.CompareAndBitpack}
   */
  public  CompareAndBitpack compareAndBitpack(Operand input, Operand threshold) {
    return CompareAndBitpack.create(scope, input, threshold);
  }

  /**
   * Adds an {@link Unstage} operation to the graph
   *
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of Unstage
   * @see {@link org.tensorflow.op.core.Unstage}
   */
  public Unstage unstage(List> dtypes, Unstage.Options... options) {
    return Unstage.create(scope, dtypes, options);
  }

  /**
   * Adds an {@link InTopKV2} operation to the graph
   *
   * @param predictions A `batch_size` x `classes` tensor.
   * @param targets A `batch_size` vector of class ids.
   * @param k Number of top elements to look at for computing precision.
   * @return a new instance of InTopKV2
   * @see {@link org.tensorflow.op.core.InTopKV2}
   */
  public  InTopKV2 inTopKV2(Operand predictions, Operand targets,
      Operand k) {
    return InTopKV2.create(scope, predictions, targets, k);
  }

  /**
   * Adds an {@link QuantizedConcat} operation to the graph
   *
   * @param concatDim 0-D.  The dimension along which to concatenate.  Must be in the
   * @param values The `N` Tensors to concatenate. Their ranks and types must match,
   * @param inputMins The minimum scalar values for each of the input tensors.
   * @param inputMaxes The maximum scalar values for each of the input tensors.
   * @return a new instance of QuantizedConcat
   * @see {@link org.tensorflow.op.core.QuantizedConcat}
   */
  public  QuantizedConcat quantizedConcat(Operand concatDim, Operand values,
      Iterable> inputMins, Iterable> inputMaxes) {
    return QuantizedConcat.create(scope, concatDim, values, inputMins, inputMaxes);
  }

  /**
   * Adds an {@link MapUnstage} operation to the graph
   *
   * @param key 
   * @param indices 
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of MapUnstage
   * @see {@link org.tensorflow.op.core.MapUnstage}
   */
  public MapUnstage mapUnstage(Operand key, Operand indices, List> dtypes,
      MapUnstage.Options... options) {
    return MapUnstage.create(scope, key, indices, dtypes, options);
  }

  /**
   * Adds an {@link Neg} operation to the graph
   *
   * @param x 
   * @return a new instance of Neg
   * @see {@link org.tensorflow.op.core.Neg}
   */
  public  Neg neg(Operand x) {
    return Neg.create(scope, x);
  }

  /**
   * Adds an {@link Betainc} operation to the graph
   *
   * @param a 
   * @param b 
   * @param x 
   * @return a new instance of Betainc
   * @see {@link org.tensorflow.op.core.Betainc}
   */
  public  Betainc betainc(Operand a, Operand b, Operand x) {
    return Betainc.create(scope, a, b, x);
  }

  /**
   * Adds an {@link ResourceSparseApplyFtrl} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param linear Should be from a Variable().
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param lr Scaling factor. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 regularization. Must be a scalar.
   * @param lrPower Scaling factor. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of ResourceSparseApplyFtrl
   * @see {@link org.tensorflow.op.core.ResourceSparseApplyFtrl}
   */
  public  ResourceSparseApplyFtrl resourceSparseApplyFtrl(Operand var,
      Operand accum, Operand linear, Operand grad, Operand indices, Operand lr,
      Operand l1, Operand l2, Operand lrPower,
      ResourceSparseApplyFtrl.Options... options) {
    return ResourceSparseApplyFtrl.create(scope, var, accum, linear, grad, indices, lr, l1, l2, lrPower, options);
  }

  /**
   * Adds an {@link ScatterNdAdd} operation to the graph
   *
   * @param ref A mutable Tensor. Should be from a Variable node.
   * @param indices A Tensor. Must be one of the following types: int32, int64.
   * @param updates A Tensor. Must have the same type as ref. A tensor of updated values
   * @param options carries optional attributes values
   * @return a new instance of ScatterNdAdd
   * @see {@link org.tensorflow.op.core.ScatterNdAdd}
   */
  public  ScatterNdAdd scatterNdAdd(Operand ref, Operand indices,
      Operand updates, ScatterNdAdd.Options... options) {
    return ScatterNdAdd.create(scope, ref, indices, updates, options);
  }

  /**
   * Adds an {@link All} operation to the graph
   *
   * @param input The tensor to reduce.
   * @param axis The dimensions to reduce. Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of All
   * @see {@link org.tensorflow.op.core.All}
   */
  public  All all(Operand input, Operand axis,
      All.Options... options) {
    return All.create(scope, input, axis, options);
  }

  /**
   * Adds an {@link L2Loss} operation to the graph
   *
   * @param t Typically 2-D, but may have any dimensions.
   * @return a new instance of L2Loss
   * @see {@link org.tensorflow.op.core.L2Loss}
   */
  public  L2Loss l2Loss(Operand t) {
    return L2Loss.create(scope, t);
  }

  /**
   * Adds an {@link Max} operation to the graph
   *
   * @param input The tensor to reduce.
   * @param axis The dimensions to reduce. Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of Max
   * @see {@link org.tensorflow.op.core.Max}
   */
  public  Max max(Operand input, Operand axis,
      Max.Options... options) {
    return Max.create(scope, input, axis, options);
  }

  /**
   * Adds an {@link CacheDataset} operation to the graph
   *
   * @param inputDataset 
   * @param filename A path on the filesystem where we should cache the dataset. Note: this
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of CacheDataset
   * @see {@link org.tensorflow.op.core.CacheDataset}
   */
  public CacheDataset cacheDataset(Operand inputDataset, Operand filename,
      List> outputTypes, List outputShapes) {
    return CacheDataset.create(scope, inputDataset, filename, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link QuantizedInstanceNorm} operation to the graph
   *
   * @param x A 4D input Tensor.
   * @param xMin The value represented by the lowest quantized input.
   * @param xMax The value represented by the highest quantized input.
   * @param options carries optional attributes values
   * @return a new instance of QuantizedInstanceNorm
   * @see {@link org.tensorflow.op.core.QuantizedInstanceNorm}
   */
  public  QuantizedInstanceNorm quantizedInstanceNorm(Operand x, Operand xMin,
      Operand xMax, QuantizedInstanceNorm.Options... options) {
    return QuantizedInstanceNorm.create(scope, x, xMin, xMax, options);
  }

  /**
   * Adds an {@link BarrierIncompleteSize} operation to the graph
   *
   * @param handle The handle to a barrier.
   * @return a new instance of BarrierIncompleteSize
   * @see {@link org.tensorflow.op.core.BarrierIncompleteSize}
   */
  public BarrierIncompleteSize barrierIncompleteSize(Operand handle) {
    return BarrierIncompleteSize.create(scope, handle);
  }

  /**
   * Adds an {@link IdentityN} operation to the graph
   *
   * @param input 
   * @return a new instance of IdentityN
   * @see {@link org.tensorflow.op.core.IdentityN}
   */
  public IdentityN identityN(Iterable> input) {
    return IdentityN.create(scope, input);
  }

  /**
   * Adds an {@link Asinh} operation to the graph
   *
   * @param x 
   * @return a new instance of Asinh
   * @see {@link org.tensorflow.op.core.Asinh}
   */
  public  Asinh asinh(Operand x) {
    return Asinh.create(scope, x);
  }

  /**
   * Adds an {@link QuantizeV2} operation to the graph
   *
   * @param input 
   * @param minRange The minimum scalar value possibly produced for the input.
   * @param maxRange The maximum scalar value possibly produced for the input.
   * @param T 
   * @param options carries optional attributes values
   * @return a new instance of QuantizeV2
   * @see {@link org.tensorflow.op.core.QuantizeV2}
   */
  public  QuantizeV2 quantizeV2(Operand input, Operand minRange,
      Operand maxRange, Class T, QuantizeV2.Options... options) {
    return QuantizeV2.create(scope, input, minRange, maxRange, T, options);
  }

  /**
   * Adds an {@link ExtractGlimpse} operation to the graph
   *
   * @param input A 4-D float tensor of shape `[batch_size, height, width, channels]`.
   * @param size A 1-D tensor of 2 elements containing the size of the glimpses
   * @param offsets A 2-D integer tensor of shape `[batch_size, 2]` containing
   * @param options carries optional attributes values
   * @return a new instance of ExtractGlimpse
   * @see {@link org.tensorflow.op.core.ExtractGlimpse}
   */
  public ExtractGlimpse extractGlimpse(Operand input, Operand size,
      Operand offsets, ExtractGlimpse.Options... options) {
    return ExtractGlimpse.create(scope, input, size, offsets, options);
  }

  /**
   * Adds an {@link EncodeWav} operation to the graph
   *
   * @param audio 2-D with shape `[length, channels]`.
   * @param sampleRate Scalar containing the sample frequency.
   * @return a new instance of EncodeWav
   * @see {@link org.tensorflow.op.core.EncodeWav}
   */
  public EncodeWav encodeWav(Operand audio, Operand sampleRate) {
    return EncodeWav.create(scope, audio, sampleRate);
  }

  /**
   * Adds an {@link StridedSliceAssign} operation to the graph
   *
   * @param ref 
   * @param begin 
   * @param end 
   * @param strides 
   * @param value 
   * @param options carries optional attributes values
   * @return a new instance of StridedSliceAssign
   * @see {@link org.tensorflow.op.core.StridedSliceAssign}
   */
  public  StridedSliceAssign stridedSliceAssign(Operand ref,
      Operand begin, Operand end, Operand strides, Operand value,
      StridedSliceAssign.Options... options) {
    return StridedSliceAssign.create(scope, ref, begin, end, strides, value, options);
  }

  /**
   * Adds an {@link AdjustHue} operation to the graph
   *
   * @param images Images to adjust.  At least 3-D.
   * @param delta A float delta to add to the hue.
   * @return a new instance of AdjustHue
   * @see {@link org.tensorflow.op.core.AdjustHue}
   */
  public AdjustHue adjustHue(Operand images, Operand delta) {
    return AdjustHue.create(scope, images, delta);
  }

  /**
   * Adds an {@link BatchMatrixSetDiag} operation to the graph
   *
   * @param input 
   * @param diagonal 
   * @return a new instance of BatchMatrixSetDiag
   * @see {@link org.tensorflow.op.core.BatchMatrixSetDiag}
   */
  public  BatchMatrixSetDiag batchMatrixSetDiag(Operand input, Operand diagonal) {
    return BatchMatrixSetDiag.create(scope, input, diagonal);
  }

  /**
   * Adds an {@link CudnnRNNBackprop} operation to the graph
   *
   * @param input 
   * @param inputH 
   * @param inputC 
   * @param params 
   * @param output 
   * @param outputH 
   * @param outputC 
   * @param outputBackprop 
   * @param outputHBackprop 
   * @param outputCBackprop 
   * @param reserveSpace 
   * @param options carries optional attributes values
   * @return a new instance of CudnnRNNBackprop
   * @see {@link org.tensorflow.op.core.CudnnRNNBackprop}
   */
  public  CudnnRNNBackprop cudnnRNNBackprop(Operand input,
      Operand inputH, Operand inputC, Operand params, Operand output,
      Operand outputH, Operand outputC, Operand outputBackprop, Operand outputHBackprop,
      Operand outputCBackprop, Operand reserveSpace, CudnnRNNBackprop.Options... options) {
    return CudnnRNNBackprop.create(scope, input, inputH, inputC, params, output, outputH, outputC, outputBackprop, outputHBackprop, outputCBackprop, reserveSpace, options);
  }

  /**
   * Adds an {@link IteratorToStringHandle} operation to the graph
   *
   * @param resourceHandle A handle to an iterator resource.
   * @return a new instance of IteratorToStringHandle
   * @see {@link org.tensorflow.op.core.IteratorToStringHandle}
   */
  public IteratorToStringHandle iteratorToStringHandle(Operand resourceHandle) {
    return IteratorToStringHandle.create(scope, resourceHandle);
  }

  /**
   * Adds an {@link GcsConfigureCredentials} operation to the graph
   *
   * @param json 
   * @return a new instance of GcsConfigureCredentials
   * @see {@link org.tensorflow.op.core.GcsConfigureCredentials}
   */
  public GcsConfigureCredentials gcsConfigureCredentials(Operand json) {
    return GcsConfigureCredentials.create(scope, json);
  }

  /**
   * Adds an {@link ConcatenateDataset} operation to the graph
   *
   * @param inputDataset 
   * @param anotherDataset 
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of ConcatenateDataset
   * @see {@link org.tensorflow.op.core.ConcatenateDataset}
   */
  public ConcatenateDataset concatenateDataset(Operand inputDataset, Operand anotherDataset,
      List> outputTypes, List outputShapes) {
    return ConcatenateDataset.create(scope, inputDataset, anotherDataset, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link ShuffleAndRepeatDataset} operation to the graph
   *
   * @param inputDataset 
   * @param bufferSize The number of output elements to buffer in an iterator over
   * @param seed A scalar seed for the random number generator. If either `seed` or
   * @param seed2 A second scalar seed to avoid seed collision.
   * @param count A scalar representing the number of times the underlying dataset
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of ShuffleAndRepeatDataset
   * @see {@link org.tensorflow.op.core.ShuffleAndRepeatDataset}
   */
  public ShuffleAndRepeatDataset shuffleAndRepeatDataset(Operand inputDataset,
      Operand bufferSize, Operand seed, Operand seed2, Operand count,
      List> outputTypes, List outputShapes) {
    return ShuffleAndRepeatDataset.create(scope, inputDataset, bufferSize, seed, seed2, count, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link Bitcast} operation to the graph
   *
   * @param input 
   * @param type 
   * @return a new instance of Bitcast
   * @see {@link org.tensorflow.op.core.Bitcast}
   */
  public  Bitcast bitcast(Operand input, Class type) {
    return Bitcast.create(scope, input, type);
  }

  /**
   * Adds an {@link MatchingFiles} operation to the graph
   *
   * @param pattern Shell wildcard pattern(s). Scalar or vector of type string.
   * @return a new instance of MatchingFiles
   * @see {@link org.tensorflow.op.core.MatchingFiles}
   */
  public MatchingFiles matchingFiles(Operand pattern) {
    return MatchingFiles.create(scope, pattern);
  }

  /**
   * Adds an {@link QueueIsClosedV2} operation to the graph
   *
   * @param handle The handle to a queue.
   * @return a new instance of QueueIsClosedV2
   * @see {@link org.tensorflow.op.core.QueueIsClosedV2}
   */
  public QueueIsClosedV2 queueIsClosedV2(Operand handle) {
    return QueueIsClosedV2.create(scope, handle);
  }

  /**
   * Adds an {@link ReaderRead} operation to the graph
   *
   * @param readerHandle Handle to a Reader.
   * @param queueHandle Handle to a Queue, with string work items.
   * @return a new instance of ReaderRead
   * @see {@link org.tensorflow.op.core.ReaderRead}
   */
  public ReaderRead readerRead(Operand readerHandle, Operand queueHandle) {
    return ReaderRead.create(scope, readerHandle, queueHandle);
  }

  /**
   * Adds an {@link Polygamma} operation to the graph
   *
   * @param a 
   * @param x 
   * @return a new instance of Polygamma
   * @see {@link org.tensorflow.op.core.Polygamma}
   */
  public  Polygamma polygamma(Operand a, Operand x) {
    return Polygamma.create(scope, a, x);
  }

  /**
   * Adds an {@link Unbatch} operation to the graph
   *
   * @param batchedTensor 
   * @param batchIndex 
   * @param id 
   * @param timeoutMicros 
   * @param options carries optional attributes values
   * @return a new instance of Unbatch
   * @see {@link org.tensorflow.op.core.Unbatch}
   */
  public  Unbatch unbatch(Operand batchedTensor, Operand batchIndex,
      Operand id, Long timeoutMicros, Unbatch.Options... options) {
    return Unbatch.create(scope, batchedTensor, batchIndex, id, timeoutMicros, options);
  }

  /**
   * Adds an {@link ScatterNd} operation to the graph
   *
   * @param indices Index tensor.
   * @param updates Updates to scatter into output.
   * @param shape 1-D. The shape of the resulting tensor.
   * @return a new instance of ScatterNd
   * @see {@link org.tensorflow.op.core.ScatterNd}
   */
  public  ScatterNd scatterNd(Operand indices, Operand updates,
      Operand shape) {
    return ScatterNd.create(scope, indices, updates, shape);
  }

  /**
   * Adds an {@link ApplyMomentum} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param grad The gradient.
   * @param momentum Momentum. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of ApplyMomentum
   * @see {@link org.tensorflow.op.core.ApplyMomentum}
   */
  public  ApplyMomentum applyMomentum(Operand var, Operand accum, Operand lr,
      Operand grad, Operand momentum, ApplyMomentum.Options... options) {
    return ApplyMomentum.create(scope, var, accum, lr, grad, momentum, options);
  }

  /**
   * Adds an {@link ScatterMax} operation to the graph
   *
   * @param ref Should be from a `Variable` node.
   * @param indices A tensor of indices into the first dimension of `ref`.
   * @param updates A tensor of updated values to reduce into `ref`.
   * @param options carries optional attributes values
   * @return a new instance of ScatterMax
   * @see {@link org.tensorflow.op.core.ScatterMax}
   */
  public  ScatterMax scatterMax(Operand ref,
      Operand indices, Operand updates, ScatterMax.Options... options) {
    return ScatterMax.create(scope, ref, indices, updates, options);
  }

  /**
   * Adds an {@link BarrierTakeMany} operation to the graph
   *
   * @param handle The handle to a barrier.
   * @param numElements A single-element tensor containing the number of elements to
   * @param componentTypes The type of each component in a value.
   * @param options carries optional attributes values
   * @return a new instance of BarrierTakeMany
   * @see {@link org.tensorflow.op.core.BarrierTakeMany}
   */
  public BarrierTakeMany barrierTakeMany(Operand handle, Operand numElements,
      List> componentTypes, BarrierTakeMany.Options... options) {
    return BarrierTakeMany.create(scope, handle, numElements, componentTypes, options);
  }

  /**
   * Adds an {@link ResourceSparseApplyAdagrad} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param lr Learning rate. Must be a scalar.
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param options carries optional attributes values
   * @return a new instance of ResourceSparseApplyAdagrad
   * @see {@link org.tensorflow.op.core.ResourceSparseApplyAdagrad}
   */
  public  ResourceSparseApplyAdagrad resourceSparseApplyAdagrad(Operand var,
      Operand accum, Operand lr, Operand grad, Operand indices,
      ResourceSparseApplyAdagrad.Options... options) {
    return ResourceSparseApplyAdagrad.create(scope, var, accum, lr, grad, indices, options);
  }

  /**
   * Adds an {@link Any} operation to the graph
   *
   * @param input The tensor to reduce.
   * @param axis The dimensions to reduce. Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of Any
   * @see {@link org.tensorflow.op.core.Any}
   */
  public  Any any(Operand input, Operand axis,
      Any.Options... options) {
    return Any.create(scope, input, axis, options);
  }

  /**
   * Adds an {@link SpaceToDepth} operation to the graph
   *
   * @param input 
   * @param blockSize The size of the spatial block.
   * @param options carries optional attributes values
   * @return a new instance of SpaceToDepth
   * @see {@link org.tensorflow.op.core.SpaceToDepth}
   */
  public  SpaceToDepth spaceToDepth(Operand input, Long blockSize,
      SpaceToDepth.Options... options) {
    return SpaceToDepth.create(scope, input, blockSize, options);
  }

  /**
   * Adds an {@link QueueIsClosed} operation to the graph
   *
   * @param handle The handle to a queue.
   * @return a new instance of QueueIsClosed
   * @see {@link org.tensorflow.op.core.QueueIsClosed}
   */
  public QueueIsClosed queueIsClosed(Operand handle) {
    return QueueIsClosed.create(scope, handle);
  }

  /**
   * Adds an {@link DepthwiseConv2dNativeBackpropInput} operation to the graph
   *
   * @param inputSizes An integer vector representing the shape of `input`, based
   * @param filter 4-D with shape
   * @param outBackprop 4-D with shape  based on `data_format`.
   * @param strides The stride of the sliding window for each dimension of the input
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of DepthwiseConv2dNativeBackpropInput
   * @see {@link org.tensorflow.op.core.DepthwiseConv2dNativeBackpropInput}
   */
  public  DepthwiseConv2dNativeBackpropInput depthwiseConv2dNativeBackpropInput(Operand inputSizes,
      Operand filter, Operand outBackprop, List strides, String padding,
      DepthwiseConv2dNativeBackpropInput.Options... options) {
    return DepthwiseConv2dNativeBackpropInput.create(scope, inputSizes, filter, outBackprop, strides, padding, options);
  }

  /**
   * Adds an {@link HSVToRGB} operation to the graph
   *
   * @param images 1-D or higher rank. HSV data to convert. Last dimension must be size 3.
   * @return a new instance of HSVToRGB
   * @see {@link org.tensorflow.op.core.HSVToRGB}
   */
  public  HSVToRGB hSVToRGB(Operand images) {
    return HSVToRGB.create(scope, images);
  }

  /**
   * Adds an {@link LookupTableSize} operation to the graph
   *
   * @param tableHandle Handle to the table.
   * @return a new instance of LookupTableSize
   * @see {@link org.tensorflow.op.core.LookupTableSize}
   */
  public LookupTableSize lookupTableSize(Operand tableHandle) {
    return LookupTableSize.create(scope, tableHandle);
  }

  /**
   * Adds an {@link Less} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of Less
   * @see {@link org.tensorflow.op.core.Less}
   */
  public  Less less(Operand x, Operand y) {
    return Less.create(scope, x, y);
  }

  /**
   * Adds an {@link ResourceScatterAdd} operation to the graph
   *
   * @param resource Should be from a `Variable` node.
   * @param indices A tensor of indices into the first dimension of `ref`.
   * @param updates A tensor of updated values to add to `ref`.
   * @return a new instance of ResourceScatterAdd
   * @see {@link org.tensorflow.op.core.ResourceScatterAdd}
   */
  public  ResourceScatterAdd resourceScatterAdd(Operand resource,
      Operand indices, Operand updates) {
    return ResourceScatterAdd.create(scope, resource, indices, updates);
  }

  /**
   * Adds an {@link ScatterAdd} operation to the graph
   *
   * @param ref Should be from a `Variable` node.
   * @param indices A tensor of indices into the first dimension of `ref`.
   * @param updates A tensor of updated values to add to `ref`.
   * @param options carries optional attributes values
   * @return a new instance of ScatterAdd
   * @see {@link org.tensorflow.op.core.ScatterAdd}
   */
  public  ScatterAdd scatterAdd(Operand ref, Operand indices,
      Operand updates, ScatterAdd.Options... options) {
    return ScatterAdd.create(scope, ref, indices, updates, options);
  }

  /**
   * Adds an {@link LogicalNot} operation to the graph
   *
   * @param x 
   * @return a new instance of LogicalNot
   * @see {@link org.tensorflow.op.core.LogicalNot}
   */
  public LogicalNot logicalNot(Operand x) {
    return LogicalNot.create(scope, x);
  }

  /**
   * Adds an {@link Cast} operation to the graph
   *
   * @param x 
   * @param DstT 
   * @return a new instance of Cast
   * @see {@link org.tensorflow.op.core.Cast}
   */
  public  Cast cast(Operand x, Class DstT) {
    return Cast.create(scope, x, DstT);
  }

  /**
   * Adds an {@link SparseAddGrad} operation to the graph
   *
   * @param backpropValGrad 1-D with shape `[nnz(sum)]`.  The gradient with respect to
   * @param aIndices 2-D.  The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`.
   * @param bIndices 2-D.  The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`.
   * @param sumIndices 2-D.  The `indices` of the sum `SparseTensor`, size
   * @return a new instance of SparseAddGrad
   * @see {@link org.tensorflow.op.core.SparseAddGrad}
   */
  public  SparseAddGrad sparseAddGrad(Operand backpropValGrad, Operand aIndices,
      Operand bIndices, Operand sumIndices) {
    return SparseAddGrad.create(scope, backpropValGrad, aIndices, bIndices, sumIndices);
  }

  /**
   * Adds an {@link ResourceSparseApplyAdadelta} operation to the graph
   *
   * @param var 
   * @param accum Should be from a Variable().
   * @param accumUpdate : Should be from a Variable().
   * @param lr Learning rate. Must be a scalar.
   * @param rho Decay factor. Must be a scalar.
   * @param epsilon Constant factor. Must be a scalar.
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param options carries optional attributes values
   * @return a new instance of ResourceSparseApplyAdadelta
   * @see {@link org.tensorflow.op.core.ResourceSparseApplyAdadelta}
   */
  public  ResourceSparseApplyAdadelta resourceSparseApplyAdadelta(Operand var,
      Operand accum, Operand accumUpdate, Operand lr, Operand rho, Operand epsilon,
      Operand grad, Operand indices, ResourceSparseApplyAdadelta.Options... options) {
    return ResourceSparseApplyAdadelta.create(scope, var, accum, accumUpdate, lr, rho, epsilon, grad, indices, options);
  }

  /**
   * Adds an {@link MatMul} operation to the graph
   *
   * @param a 
   * @param b 
   * @param options carries optional attributes values
   * @return a new instance of MatMul
   * @see {@link org.tensorflow.op.core.MatMul}
   */
  public  MatMul matMul(Operand a, Operand b, MatMul.Options... options) {
    return MatMul.create(scope, a, b, options);
  }

  /**
   * Adds an {@link Softmax} operation to the graph
   *
   * @param logits 2-D with shape `[batch_size, num_classes]`.
   * @return a new instance of Softmax
   * @see {@link org.tensorflow.op.core.Softmax}
   */
  public  Softmax softmax(Operand logits) {
    return Softmax.create(scope, logits);
  }

  /**
   * Adds an {@link MergeSummary} operation to the graph
   *
   * @param inputs Can be of any shape.  Each must contain serialized `Summary` protocol
   * @return a new instance of MergeSummary
   * @see {@link org.tensorflow.op.core.MergeSummary}
   */
  public MergeSummary mergeSummary(Iterable> inputs) {
    return MergeSummary.create(scope, inputs);
  }

  /**
   * Adds an {@link BatchSvd} operation to the graph
   *
   * @param input 
   * @param options carries optional attributes values
   * @return a new instance of BatchSvd
   * @see {@link org.tensorflow.op.core.BatchSvd}
   */
  public  BatchSvd batchSvd(Operand input, BatchSvd.Options... options) {
    return BatchSvd.create(scope, input, options);
  }

  /**
   * Adds an {@link QuantizedReshape} operation to the graph
   *
   * @param tensor 
   * @param shape Defines the shape of the output tensor.
   * @param inputMin The minimum value of the input.
   * @param inputMax The maximum value of the input.
   * @return a new instance of QuantizedReshape
   * @see {@link org.tensorflow.op.core.QuantizedReshape}
   */
  public  QuantizedReshape quantizedReshape(Operand tensor,
      Operand shape, Operand inputMin, Operand inputMax) {
    return QuantizedReshape.create(scope, tensor, shape, inputMin, inputMax);
  }

  /**
   * Adds an {@link Softsign} operation to the graph
   *
   * @param features 
   * @return a new instance of Softsign
   * @see {@link org.tensorflow.op.core.Softsign}
   */
  public  Softsign softsign(Operand features) {
    return Softsign.create(scope, features);
  }

  /**
   * Adds an {@link TensorArraySize} operation to the graph
   *
   * @param handle The handle to a TensorArray (output of TensorArray or TensorArrayGrad).
   * @param flowIn A float scalar that enforces proper chaining of operations.
   * @return a new instance of TensorArraySize
   * @see {@link org.tensorflow.op.core.TensorArraySize}
   */
  public TensorArraySize tensorArraySize(Operand handle, Operand flowIn) {
    return TensorArraySize.create(scope, handle, flowIn);
  }

  /**
   * Adds an {@link MatrixExponential} operation to the graph
   *
   * @param input Shape is `[..., M, M]`.
   * @return a new instance of MatrixExponential
   * @see {@link org.tensorflow.op.core.MatrixExponential}
   */
  public  MatrixExponential matrixExponential(Operand input) {
    return MatrixExponential.create(scope, input);
  }

  /**
   * Adds an {@link QuantizedConv2D} operation to the graph
   *
   * @param input 
   * @param filter filter's input_depth dimension must match input's depth dimensions.
   * @param minInput The float value that the lowest quantized input value represents.
   * @param maxInput The float value that the highest quantized input value represents.
   * @param minFilter The float value that the lowest quantized filter value represents.
   * @param maxFilter The float value that the highest quantized filter value represents.
   * @param outType 
   * @param strides The stride of the sliding window for each dimension of the input
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of QuantizedConv2D
   * @see {@link org.tensorflow.op.core.QuantizedConv2D}
   */
  public  QuantizedConv2D quantizedConv2D(Operand input, Operand filter,
      Operand minInput, Operand maxInput, Operand minFilter,
      Operand maxFilter, Class outType, List strides, String padding,
      QuantizedConv2D.Options... options) {
    return QuantizedConv2D.create(scope, input, filter, minInput, maxInput, minFilter, maxFilter, outType, strides, padding, options);
  }

  /**
   * Adds an {@link Atan} operation to the graph
   *
   * @param x 
   * @return a new instance of Atan
   * @see {@link org.tensorflow.op.core.Atan}
   */
  public  Atan atan(Operand x) {
    return Atan.create(scope, x);
  }

  /**
   * Adds an {@link ResourceApplyPowerSign} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param m Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param logbase Must be a scalar.
   * @param signDecay Must be a scalar.
   * @param beta Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ResourceApplyPowerSign
   * @see {@link org.tensorflow.op.core.ResourceApplyPowerSign}
   */
  public  ResourceApplyPowerSign resourceApplyPowerSign(Operand var, Operand m,
      Operand lr, Operand logbase, Operand signDecay, Operand beta, Operand grad,
      ResourceApplyPowerSign.Options... options) {
    return ResourceApplyPowerSign.create(scope, var, m, lr, logbase, signDecay, beta, grad, options);
  }

  /**
   * Adds an {@link PriorityQueue} operation to the graph
   *
   * @param componentTypes The type of each component in a value.
   * @param shapes The shape of each component in a value. The length of this attr must
   * @param options carries optional attributes values
   * @return a new instance of PriorityQueue
   * @see {@link org.tensorflow.op.core.PriorityQueue}
   */
  public PriorityQueue priorityQueue(List> componentTypes, List shapes,
      PriorityQueue.Options... options) {
    return PriorityQueue.create(scope, componentTypes, shapes, options);
  }

  /**
   * Adds an {@link ResourceApplyAdagradDA} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param gradientAccumulator Should be from a Variable().
   * @param gradientSquaredAccumulator Should be from a Variable().
   * @param grad The gradient.
   * @param lr Scaling factor. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 regularization. Must be a scalar.
   * @param globalStep Training step number. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of ResourceApplyAdagradDA
   * @see {@link org.tensorflow.op.core.ResourceApplyAdagradDA}
   */
  public  ResourceApplyAdagradDA resourceApplyAdagradDA(Operand var,
      Operand gradientAccumulator, Operand gradientSquaredAccumulator, Operand grad,
      Operand lr, Operand l1, Operand l2, Operand globalStep,
      ResourceApplyAdagradDA.Options... options) {
    return ResourceApplyAdagradDA.create(scope, var, gradientAccumulator, gradientSquaredAccumulator, grad, lr, l1, l2, globalStep, options);
  }

  /**
   * Adds an {@link Constant} operation to the graph
   *
   * @param shape the tensor shape.
   * @param data a buffer containing the tensor data.
   * @throws IllegalArgumentException If the tensor shape is not compatible with the buffer
   * @see {@link org.tensorflow.op.core.Constant}
   */
  public Constant constant(long[] shape, FloatBuffer data) {
    return Constant.create(scope, shape, data);
  }

  /**
   * Adds an {@link RepeatDataset} operation to the graph
   *
   * @param inputDataset 
   * @param count A scalar representing the number of times that `input_dataset` should
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of RepeatDataset
   * @see {@link org.tensorflow.op.core.RepeatDataset}
   */
  public RepeatDataset repeatDataset(Operand inputDataset, Operand count,
      List> outputTypes, List outputShapes) {
    return RepeatDataset.create(scope, inputDataset, count, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link PaddedBatchDataset} operation to the graph
   *
   * @param inputDataset 
   * @param batchSize A scalar representing the number of elements to accumulate in a
   * @param paddedShapes A list of int64 tensors representing the desired padded shapes
   * @param paddingValues A list of scalars containing the padding value to use for
   * @param outputShapes 
   * @return a new instance of PaddedBatchDataset
   * @see {@link org.tensorflow.op.core.PaddedBatchDataset}
   */
  public PaddedBatchDataset paddedBatchDataset(Operand inputDataset, Operand batchSize,
      Iterable> paddedShapes, Iterable> paddingValues,
      List outputShapes) {
    return PaddedBatchDataset.create(scope, inputDataset, batchSize, paddedShapes, paddingValues, outputShapes);
  }

  /**
   * Adds an {@link MapStage} operation to the graph
   *
   * @param key int64
   * @param indices 
   * @param values a list of tensors
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of MapStage
   * @see {@link org.tensorflow.op.core.MapStage}
   */
  public MapStage mapStage(Operand key, Operand indices, Iterable> values,
      List> dtypes, MapStage.Options... options) {
    return MapStage.create(scope, key, indices, values, dtypes, options);
  }

  /**
   * Adds an {@link SdcaFprint} operation to the graph
   *
   * @param input vector of strings to compute fingerprints on.
   * @return a new instance of SdcaFprint
   * @see {@link org.tensorflow.op.core.SdcaFprint}
   */
  public SdcaFprint sdcaFprint(Operand input) {
    return SdcaFprint.create(scope, input);
  }

  /**
   * Adds an {@link OrderedMapIncompleteSize} operation to the graph
   *
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of OrderedMapIncompleteSize
   * @see {@link org.tensorflow.op.core.OrderedMapIncompleteSize}
   */
  public OrderedMapIncompleteSize orderedMapIncompleteSize(List> dtypes,
      OrderedMapIncompleteSize.Options... options) {
    return OrderedMapIncompleteSize.create(scope, dtypes, options);
  }

  /**
   * Adds an {@link CropAndResizeGradImage} operation to the graph
   *
   * @param grads A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
   * @param boxes A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor
   * @param boxInd A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
   * @param imageSize A 1-D tensor with value `[batch, image_height, image_width, depth]`
   * @param T 
   * @param options carries optional attributes values
   * @return a new instance of CropAndResizeGradImage
   * @see {@link org.tensorflow.op.core.CropAndResizeGradImage}
   */
  public  CropAndResizeGradImage cropAndResizeGradImage(Operand grads,
      Operand boxes, Operand boxInd, Operand imageSize, Class T,
      CropAndResizeGradImage.Options... options) {
    return CropAndResizeGradImage.create(scope, grads, boxes, boxInd, imageSize, T, options);
  }

  /**
   * Adds an {@link ResourceApplyAdadelta} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param accumUpdate Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param rho Decay factor. Must be a scalar.
   * @param epsilon Constant factor. Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ResourceApplyAdadelta
   * @see {@link org.tensorflow.op.core.ResourceApplyAdadelta}
   */
  public  ResourceApplyAdadelta resourceApplyAdadelta(Operand var, Operand accum,
      Operand accumUpdate, Operand lr, Operand rho, Operand epsilon, Operand grad,
      ResourceApplyAdadelta.Options... options) {
    return ResourceApplyAdadelta.create(scope, var, accum, accumUpdate, lr, rho, epsilon, grad, options);
  }

  /**
   * Adds an {@link SerializeSparse} operation to the graph
   *
   * @param sparseIndices 2-D.  The `indices` of the `SparseTensor`.
   * @param sparseValues 1-D.  The `values` of the `SparseTensor`.
   * @param sparseShape 1-D.  The `shape` of the `SparseTensor`.
   * @param outType The `dtype` to use for serialization; the supported types are `string`
   * @return a new instance of SerializeSparse
   * @see {@link org.tensorflow.op.core.SerializeSparse}
   */
  public  SerializeSparse serializeSparse(Operand sparseIndices,
      Operand sparseValues, Operand sparseShape, Class outType) {
    return SerializeSparse.create(scope, sparseIndices, sparseValues, sparseShape, outType);
  }

  /**
   * Adds an {@link AssignAdd} operation to the graph
   *
   * @param ref Should be from a `Variable` node.
   * @param value The value to be added to the variable.
   * @param options carries optional attributes values
   * @return a new instance of AssignAdd
   * @see {@link org.tensorflow.op.core.AssignAdd}
   */
  public  AssignAdd assignAdd(Operand ref, Operand value,
      AssignAdd.Options... options) {
    return AssignAdd.create(scope, ref, value, options);
  }

  /**
   * Adds an {@link StatelessMultinomial} operation to the graph
   *
   * @param logits 2-D Tensor with shape `[batch_size, num_classes]`.  Each slice `[i, :]`
   * @param numSamples 0-D.  Number of independent samples to draw for each row slice.
   * @param seed 2 seeds (shape [2]).
   * @param outputDtype 
   * @return a new instance of StatelessMultinomial
   * @see {@link org.tensorflow.op.core.StatelessMultinomial}
   */
  public  StatelessMultinomial statelessMultinomial(Operand logits,
      Operand numSamples, Operand seed, Class outputDtype) {
    return StatelessMultinomial.create(scope, logits, numSamples, seed, outputDtype);
  }

  /**
   * Adds an {@link GetSessionTensor} operation to the graph
   *
   * @param handle The handle for a tensor stored in the session state.
   * @param dtype The type of the output value.
   * @return a new instance of GetSessionTensor
   * @see {@link org.tensorflow.op.core.GetSessionTensor}
   */
  public  GetSessionTensor getSessionTensor(Operand handle, Class dtype) {
    return GetSessionTensor.create(scope, handle, dtype);
  }

  /**
   * Adds an {@link SparseApplyRMSProp} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param ms Should be from a Variable().
   * @param mom Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param rho Decay rate. Must be a scalar.
   * @param momentum 
   * @param epsilon Ridge term. Must be a scalar.
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var, ms and mom.
   * @param options carries optional attributes values
   * @return a new instance of SparseApplyRMSProp
   * @see {@link org.tensorflow.op.core.SparseApplyRMSProp}
   */
  public  SparseApplyRMSProp sparseApplyRMSProp(Operand var,
      Operand ms, Operand mom, Operand lr, Operand rho, Operand momentum,
      Operand epsilon, Operand grad, Operand indices,
      SparseApplyRMSProp.Options... options) {
    return SparseApplyRMSProp.create(scope, var, ms, mom, lr, rho, momentum, epsilon, grad, indices, options);
  }

  /**
   * Adds an {@link StringToHashBucketFast} operation to the graph
   *
   * @param input The strings to assign a hash bucket.
   * @param numBuckets The number of buckets.
   * @return a new instance of StringToHashBucketFast
   * @see {@link org.tensorflow.op.core.StringToHashBucketFast}
   */
  public StringToHashBucketFast stringToHashBucketFast(Operand input, Long numBuckets) {
    return StringToHashBucketFast.create(scope, input, numBuckets);
  }

  /**
   * Adds an {@link FakeQuantWithMinMaxArgsGradient} operation to the graph
   *
   * @param gradients Backpropagated gradients above the FakeQuantWithMinMaxArgs operation.
   * @param inputs Values passed as inputs to the FakeQuantWithMinMaxArgs operation.
   * @param options carries optional attributes values
   * @return a new instance of FakeQuantWithMinMaxArgsGradient
   * @see {@link org.tensorflow.op.core.FakeQuantWithMinMaxArgsGradient}
   */
  public FakeQuantWithMinMaxArgsGradient fakeQuantWithMinMaxArgsGradient(Operand gradients,
      Operand inputs, FakeQuantWithMinMaxArgsGradient.Options... options) {
    return FakeQuantWithMinMaxArgsGradient.create(scope, gradients, inputs, options);
  }

  /**
   * Adds an {@link BatchMatrixDiagPart} operation to the graph
   *
   * @param input 
   * @return a new instance of BatchMatrixDiagPart
   * @see {@link org.tensorflow.op.core.BatchMatrixDiagPart}
   */
  public  BatchMatrixDiagPart batchMatrixDiagPart(Operand input) {
    return BatchMatrixDiagPart.create(scope, input);
  }

  /**
   * Adds an {@link LogUniformCandidateSampler} operation to the graph
   *
   * @param trueClasses A batch_size * num_true matrix, in which each row contains the
   * @param numTrue Number of true labels per context.
   * @param numSampled Number of candidates to randomly sample.
   * @param unique If unique is true, we sample with rejection, so that all sampled
   * @param rangeMax The sampler will sample integers from the interval [0, range_max).
   * @param options carries optional attributes values
   * @return a new instance of LogUniformCandidateSampler
   * @see {@link org.tensorflow.op.core.LogUniformCandidateSampler}
   */
  public LogUniformCandidateSampler logUniformCandidateSampler(Operand trueClasses,
      Long numTrue, Long numSampled, Boolean unique, Long rangeMax,
      LogUniformCandidateSampler.Options... options) {
    return LogUniformCandidateSampler.create(scope, trueClasses, numTrue, numSampled, unique, rangeMax, options);
  }

  /**
   * Adds an {@link AssignAddVariableOp} operation to the graph
   *
   * @param resource handle to the resource in which to store the variable.
   * @param value the value by which the variable will be incremented.
   * @return a new instance of AssignAddVariableOp
   * @see {@link org.tensorflow.op.core.AssignAddVariableOp}
   */
  public  AssignAddVariableOp assignAddVariableOp(Operand resource, Operand value) {
    return AssignAddVariableOp.create(scope, resource, value);
  }

  /**
   * Adds an {@link SaveSlices} operation to the graph
   *
   * @param filename Must have a single element. The name of the file to which we write the
   * @param tensorNames Shape `[N]`. The names of the tensors to be saved.
   * @param shapesAndSlices Shape `[N]`.  The shapes and slice specifications to use when
   * @param data `N` tensors to save.
   * @return a new instance of SaveSlices
   * @see {@link org.tensorflow.op.core.SaveSlices}
   */
  public SaveSlices saveSlices(Operand filename, Operand tensorNames,
      Operand shapesAndSlices, Iterable> data) {
    return SaveSlices.create(scope, filename, tensorNames, shapesAndSlices, data);
  }

  /**
   * Adds an {@link Gather} operation to the graph
   *
   * @param params 
   * @param indices 
   * @param options carries optional attributes values
   * @return a new instance of Gather
   * @see {@link org.tensorflow.op.core.Gather}
   */
  public  Gather gather(Operand params, Operand indices,
      Gather.Options... options) {
    return Gather.create(scope, params, indices, options);
  }

  /**
   * Adds an {@link StatelessTruncatedNormal} operation to the graph
   *
   * @param shape The shape of the output tensor.
   * @param seed 2 seeds (shape [2]).
   * @param dtype The type of the output.
   * @return a new instance of StatelessTruncatedNormal
   * @see {@link org.tensorflow.op.core.StatelessTruncatedNormal}
   */
  public  StatelessTruncatedNormal statelessTruncatedNormal(Operand shape,
      Operand seed, Class dtype) {
    return StatelessTruncatedNormal.create(scope, shape, seed, dtype);
  }

  /**
   * Adds an {@link NonMaxSuppressionV3} operation to the graph
   *
   * @param boxes A 2-D float tensor of shape `[num_boxes, 4]`.
   * @param scores A 1-D float tensor of shape `[num_boxes]` representing a single
   * @param maxOutputSize A scalar integer tensor representing the maximum number of
   * @param iouThreshold A 0-D float tensor representing the threshold for deciding whether
   * @param scoreThreshold A 0-D float tensor representing the threshold for deciding when to remove
   * @return a new instance of NonMaxSuppressionV3
   * @see {@link org.tensorflow.op.core.NonMaxSuppressionV3}
   */
  public NonMaxSuppressionV3 nonMaxSuppressionV3(Operand boxes, Operand scores,
      Operand maxOutputSize, Operand iouThreshold, Operand scoreThreshold) {
    return NonMaxSuppressionV3.create(scope, boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
  }

  /**
   * Adds an {@link Relu} operation to the graph
   *
   * @param features 
   * @return a new instance of Relu
   * @see {@link org.tensorflow.op.core.Relu}
   */
  public  Relu relu(Operand features) {
    return Relu.create(scope, features);
  }

  /**
   * Adds an {@link MatrixSolveLs} operation to the graph
   *
   * @param matrix Shape is `[..., M, N]`.
   * @param rhs Shape is `[..., M, K]`.
   * @param l2Regularizer Scalar tensor.
   * @param options carries optional attributes values
   * @return a new instance of MatrixSolveLs
   * @see {@link org.tensorflow.op.core.MatrixSolveLs}
   */
  public  MatrixSolveLs matrixSolveLs(Operand matrix, Operand rhs,
      Operand l2Regularizer, MatrixSolveLs.Options... options) {
    return MatrixSolveLs.create(scope, matrix, rhs, l2Regularizer, options);
  }

  /**
   * Adds an {@link SparseReduceMax} operation to the graph
   *
   * @param inputIndices 2-D.  `N x R` matrix with the indices of non-empty values in a
   * @param inputValues 1-D.  `N` non-empty values corresponding to `input_indices`.
   * @param inputShape 1-D.  Shape of the input SparseTensor.
   * @param reductionAxes 1-D.  Length-`K` vector containing the reduction axes.
   * @param options carries optional attributes values
   * @return a new instance of SparseReduceMax
   * @see {@link org.tensorflow.op.core.SparseReduceMax}
   */
  public  SparseReduceMax sparseReduceMax(Operand inputIndices,
      Operand inputValues, Operand inputShape, Operand reductionAxes,
      SparseReduceMax.Options... options) {
    return SparseReduceMax.create(scope, inputIndices, inputValues, inputShape, reductionAxes, options);
  }

  /**
   * Adds an {@link ResourceApplyGradientDescent} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param alpha Scaling factor. Must be a scalar.
   * @param delta The change.
   * @param options carries optional attributes values
   * @return a new instance of ResourceApplyGradientDescent
   * @see {@link org.tensorflow.op.core.ResourceApplyGradientDescent}
   */
  public  ResourceApplyGradientDescent resourceApplyGradientDescent(Operand var,
      Operand alpha, Operand delta, ResourceApplyGradientDescent.Options... options) {
    return ResourceApplyGradientDescent.create(scope, var, alpha, delta, options);
  }

  /**
   * Adds an {@link MatrixDiag} operation to the graph
   *
   * @param diagonal Rank `k`, where `k >= 1`.
   * @return a new instance of MatrixDiag
   * @see {@link org.tensorflow.op.core.MatrixDiag}
   */
  public  MatrixDiag matrixDiag(Operand diagonal) {
    return MatrixDiag.create(scope, diagonal);
  }

  /**
   * Adds an {@link ShapeN} operation to the graph
   *
   * @param input 
   * @param outType 
   * @return a new instance of ShapeN
   * @see {@link org.tensorflow.op.core.ShapeN}
   */
  public  ShapeN shapeN(Operand input, Class outType) {
    return ShapeN.create(scope, input, outType);
  }

  /**
   * Adds an {@link SparseApplyAdagradDA} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param gradientAccumulator Should be from a Variable().
   * @param gradientSquaredAccumulator Should be from a Variable().
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param lr Learning rate. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 regularization. Must be a scalar.
   * @param globalStep Training step number. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of SparseApplyAdagradDA
   * @see {@link org.tensorflow.op.core.SparseApplyAdagradDA}
   */
  public  SparseApplyAdagradDA sparseApplyAdagradDA(Operand var,
      Operand gradientAccumulator, Operand gradientSquaredAccumulator, Operand grad,
      Operand indices, Operand lr, Operand l1, Operand l2, Operand globalStep,
      SparseApplyAdagradDA.Options... options) {
    return SparseApplyAdagradDA.create(scope, var, gradientAccumulator, gradientSquaredAccumulator, grad, indices, lr, l1, l2, globalStep, options);
  }

  /**
   * Adds an {@link StringToHashBucketStrong} operation to the graph
   *
   * @param input The strings to assign a hash bucket.
   * @param numBuckets The number of buckets.
   * @param key The key for the keyed hash function passed as a list of two uint64
   * @return a new instance of StringToHashBucketStrong
   * @see {@link org.tensorflow.op.core.StringToHashBucketStrong}
   */
  public StringToHashBucketStrong stringToHashBucketStrong(Operand input, Long numBuckets,
      List key) {
    return StringToHashBucketStrong.create(scope, input, numBuckets, key);
  }

  /**
   * Adds an {@link FractionalMaxPool} operation to the graph
   *
   * @param value 4-D with shape `[batch, height, width, channels]`.
   * @param poolingRatio Pooling ratio for each dimension of `value`, currently only
   * @param options carries optional attributes values
   * @return a new instance of FractionalMaxPool
   * @see {@link org.tensorflow.op.core.FractionalMaxPool}
   */
  public  FractionalMaxPool fractionalMaxPool(Operand value,
      List poolingRatio, FractionalMaxPool.Options... options) {
    return FractionalMaxPool.create(scope, value, poolingRatio, options);
  }

  /**
   * Adds an {@link Pad} operation to the graph
   *
   * @param input 
   * @param paddings 
   * @return a new instance of Pad
   * @see {@link org.tensorflow.op.core.Pad}
   */
  public  Pad pad(Operand input, Operand paddings) {
    return Pad.create(scope, input, paddings);
  }

  /**
   * Adds an {@link AnonymousIterator} operation to the graph
   *
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of AnonymousIterator
   * @see {@link org.tensorflow.op.core.AnonymousIterator}
   */
  public AnonymousIterator anonymousIterator(List> outputTypes, List outputShapes) {
    return AnonymousIterator.create(scope, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link SparseDenseCwiseAdd} operation to the graph
   *
   * @param spIndices 2-D.  `N x R` matrix with the indices of non-empty values in a
   * @param spValues 1-D.  `N` non-empty values corresponding to `sp_indices`.
   * @param spShape 1-D.  Shape of the input SparseTensor.
   * @param dense `R`-D.  The dense Tensor operand.
   * @return a new instance of SparseDenseCwiseAdd
   * @see {@link org.tensorflow.op.core.SparseDenseCwiseAdd}
   */
  public  SparseDenseCwiseAdd sparseDenseCwiseAdd(Operand spIndices,
      Operand spValues, Operand spShape, Operand dense) {
    return SparseDenseCwiseAdd.create(scope, spIndices, spValues, spShape, dense);
  }

  /**
   * Adds an {@link DataFormatDimMap} operation to the graph
   *
   * @param x A Tensor with each element as a dimension index in source data format.
   * @param options carries optional attributes values
   * @return a new instance of DataFormatDimMap
   * @see {@link org.tensorflow.op.core.DataFormatDimMap}
   */
  public  DataFormatDimMap dataFormatDimMap(Operand x,
      DataFormatDimMap.Options... options) {
    return DataFormatDimMap.create(scope, x, options);
  }

  /**
   * Adds an {@link Substr} operation to the graph
   *
   * @param input Tensor of strings
   * @param pos Scalar defining the position of first character in each substring
   * @param len Scalar defining the number of characters to include in each substring
   * @return a new instance of Substr
   * @see {@link org.tensorflow.op.core.Substr}
   */
  public  Substr substr(Operand input, Operand pos, Operand len) {
    return Substr.create(scope, input, pos, len);
  }

  /**
   * Adds an {@link DecodeJSONExample} operation to the graph
   *
   * @param jsonExamples Each string is a JSON object serialized according to the JSON
   * @return a new instance of DecodeJSONExample
   * @see {@link org.tensorflow.op.core.DecodeJSONExample}
   */
  public DecodeJSONExample decodeJSONExample(Operand jsonExamples) {
    return DecodeJSONExample.create(scope, jsonExamples);
  }

  /**
   * Adds an {@link RandomShuffle} operation to the graph
   *
   * @param value The tensor to be shuffled.
   * @param options carries optional attributes values
   * @return a new instance of RandomShuffle
   * @see {@link org.tensorflow.op.core.RandomShuffle}
   */
  public  RandomShuffle randomShuffle(Operand value, RandomShuffle.Options... options) {
    return RandomShuffle.create(scope, value, options);
  }

  /**
   * Adds an {@link AccumulateNV2} operation to the graph
   *
   * @param inputs A list of `Tensor` objects, each with same shape and type.
   * @param shape Shape of elements of `inputs`.
   * @return a new instance of AccumulateNV2
   * @see {@link org.tensorflow.op.core.AccumulateNV2}
   */
  public  AccumulateNV2 accumulateNV2(Operand inputs, Shape shape) {
    return AccumulateNV2.create(scope, inputs, shape);
  }

  /**
   * Adds an {@link QuantizedBatchNormWithGlobalNormalization} operation to the graph
   *
   * @param t A 4D input Tensor.
   * @param tMin The value represented by the lowest quantized input.
   * @param tMax The value represented by the highest quantized input.
   * @param m A 1D mean Tensor with size matching the last dimension of t.
   * @param mMin The value represented by the lowest quantized mean.
   * @param mMax The value represented by the highest quantized mean.
   * @param v A 1D variance Tensor with size matching the last dimension of t.
   * @param vMin The value represented by the lowest quantized variance.
   * @param vMax The value represented by the highest quantized variance.
   * @param beta A 1D beta Tensor with size matching the last dimension of t.
   * @param betaMin The value represented by the lowest quantized offset.
   * @param betaMax The value represented by the highest quantized offset.
   * @param gamma A 1D gamma Tensor with size matching the last dimension of t.
   * @param gammaMin The value represented by the lowest quantized gamma.
   * @param gammaMax The value represented by the highest quantized gamma.
   * @param outType 
   * @param varianceEpsilon A small float number to avoid dividing by 0.
   * @param scaleAfterNormalization A bool indicating whether the resulted tensor
   * @return a new instance of QuantizedBatchNormWithGlobalNormalization
   * @see {@link org.tensorflow.op.core.QuantizedBatchNormWithGlobalNormalization}
   */
  public  QuantizedBatchNormWithGlobalNormalization quantizedBatchNormWithGlobalNormalization(Operand t,
      Operand tMin, Operand tMax, Operand m, Operand mMin,
      Operand mMax, Operand v, Operand vMin, Operand vMax, Operand beta,
      Operand betaMin, Operand betaMax, Operand gamma, Operand gammaMin,
      Operand gammaMax, Class outType, Float varianceEpsilon,
      Boolean scaleAfterNormalization) {
    return QuantizedBatchNormWithGlobalNormalization.create(scope, t, tMin, tMax, m, mMin, mMax, v, vMin, vMax, beta, betaMin, betaMax, gamma, gammaMin, gammaMax, outType, varianceEpsilon, scaleAfterNormalization);
  }

  /**
   * Adds an {@link GatherNd} operation to the graph
   *
   * @param params The tensor from which to gather values.
   * @param indices Index tensor.
   * @return a new instance of GatherNd
   * @see {@link org.tensorflow.op.core.GatherNd}
   */
  public  GatherNd gatherNd(Operand params, Operand indices) {
    return GatherNd.create(scope, params, indices);
  }

  /**
   * Adds an {@link ResourceApplyCenteredRMSProp} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param mg Should be from a Variable().
   * @param ms Should be from a Variable().
   * @param mom Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param rho Decay rate. Must be a scalar.
   * @param momentum 
   * @param epsilon Ridge term. Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ResourceApplyCenteredRMSProp
   * @see {@link org.tensorflow.op.core.ResourceApplyCenteredRMSProp}
   */
  public  ResourceApplyCenteredRMSProp resourceApplyCenteredRMSProp(Operand var,
      Operand mg, Operand ms, Operand mom, Operand lr, Operand rho,
      Operand momentum, Operand epsilon, Operand grad,
      ResourceApplyCenteredRMSProp.Options... options) {
    return ResourceApplyCenteredRMSProp.create(scope, var, mg, ms, mom, lr, rho, momentum, epsilon, grad, options);
  }

  /**
   * Adds an {@link FusedResizeAndPadConv2D} operation to the graph
   *
   * @param input 4-D with shape `[batch, in_height, in_width, in_channels]`.
   * @param size A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
   * @param paddings A two-column matrix specifying the padding sizes. The number of
   * @param filter 4-D with shape
   * @param mode 
   * @param strides 1-D of length 4.  The stride of the sliding window for each dimension
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of FusedResizeAndPadConv2D
   * @see {@link org.tensorflow.op.core.FusedResizeAndPadConv2D}
   */
  public  FusedResizeAndPadConv2D fusedResizeAndPadConv2D(Operand input,
      Operand size, Operand paddings, Operand filter, String mode,
      List strides, String padding, FusedResizeAndPadConv2D.Options... options) {
    return FusedResizeAndPadConv2D.create(scope, input, size, paddings, filter, mode, strides, padding, options);
  }

  /**
   * Adds an {@link CTCGreedyDecoder} operation to the graph
   *
   * @param inputs 3-D, shape: `(max_time x batch_size x num_classes)`, the logits.
   * @param sequenceLength A vector containing sequence lengths, size `(batch_size)`.
   * @param options carries optional attributes values
   * @return a new instance of CTCGreedyDecoder
   * @see {@link org.tensorflow.op.core.CTCGreedyDecoder}
   */
  public CTCGreedyDecoder cTCGreedyDecoder(Operand inputs, Operand sequenceLength,
      CTCGreedyDecoder.Options... options) {
    return CTCGreedyDecoder.create(scope, inputs, sequenceLength, options);
  }

  /**
   * Adds an {@link HistogramSummary} operation to the graph
   *
   * @param tag Scalar.  Tag to use for the `Summary.Value`.
   * @param values Any shape. Values to use to build the histogram.
   * @return a new instance of HistogramSummary
   * @see {@link org.tensorflow.op.core.HistogramSummary}
   */
  public  HistogramSummary histogramSummary(Operand tag,
      Operand values) {
    return HistogramSummary.create(scope, tag, values);
  }

  /**
   * Adds an {@link Requantize} operation to the graph
   *
   * @param input 
   * @param inputMin The float value that the minimum quantized input value represents.
   * @param inputMax The float value that the maximum quantized input value represents.
   * @param requestedOutputMin The float value that the minimum quantized output value represents.
   * @param requestedOutputMax The float value that the maximum quantized output value represents.
   * @param outType The type of the output. Should be a lower bit depth than Tinput.
   * @return a new instance of Requantize
   * @see {@link org.tensorflow.op.core.Requantize}
   */
  public  Requantize requantize(Operand input, Operand inputMin,
      Operand inputMax, Operand requestedOutputMin, Operand requestedOutputMax,
      Class outType) {
    return Requantize.create(scope, input, inputMin, inputMax, requestedOutputMin, requestedOutputMax, outType);
  }

  /**
   * Adds an {@link Sinh} operation to the graph
   *
   * @param x 
   * @return a new instance of Sinh
   * @see {@link org.tensorflow.op.core.Sinh}
   */
  public  Sinh sinh(Operand x) {
    return Sinh.create(scope, x);
  }

  /**
   * Adds an {@link Rsqrt} operation to the graph
   *
   * @param x 
   * @return a new instance of Rsqrt
   * @see {@link org.tensorflow.op.core.Rsqrt}
   */
  public  Rsqrt rsqrt(Operand x) {
    return Rsqrt.create(scope, x);
  }

  /**
   * Adds an {@link OneHot} operation to the graph
   *
   * @param indices A tensor of indices.
   * @param depth A scalar defining the depth of the one hot dimension.
   * @param onValue A scalar defining the value to fill in output when `indices[j] = i`.
   * @param offValue A scalar defining the value to fill in output when `indices[j] != i`.
   * @param options carries optional attributes values
   * @return a new instance of OneHot
   * @see {@link org.tensorflow.op.core.OneHot}
   */
  public  OneHot oneHot(Operand indices, Operand depth,
      Operand onValue, Operand offValue, OneHot.Options... options) {
    return OneHot.create(scope, indices, depth, onValue, offValue, options);
  }

  /**
   * Adds an {@link Sqrt} operation to the graph
   *
   * @param x 
   * @return a new instance of Sqrt
   * @see {@link org.tensorflow.op.core.Sqrt}
   */
  public  Sqrt sqrt(Operand x) {
    return Sqrt.create(scope, x);
  }

  /**
   * Adds an {@link BatchToSpaceND} operation to the graph
   *
   * @param input N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,
   * @param blockShape 1-D with shape `[M]`, all values must be >= 1.
   * @param crops 2-D with shape `[M, 2]`, all values must be >= 0.
   * @return a new instance of BatchToSpaceND
   * @see {@link org.tensorflow.op.core.BatchToSpaceND}
   */
  public  BatchToSpaceND batchToSpaceND(Operand input,
      Operand blockShape, Operand crops) {
    return BatchToSpaceND.create(scope, input, blockShape, crops);
  }

  /**
   * Adds an {@link DestroyTemporaryVariable} operation to the graph
   *
   * @param ref A reference to the temporary variable tensor.
   * @param varName Name of the temporary variable, usually the name of the matching
   * @return a new instance of DestroyTemporaryVariable
   * @see {@link org.tensorflow.op.core.DestroyTemporaryVariable}
   */
  public  DestroyTemporaryVariable destroyTemporaryVariable(Operand ref, String varName) {
    return DestroyTemporaryVariable.create(scope, ref, varName);
  }

  /**
   * Adds an {@link IteratorFromStringHandle} operation to the graph
   *
   * @param stringHandle A string representation of the given handle.
   * @param outputTypes If specified, defines the type of each tuple component in an
   * @param options carries optional attributes values
   * @return a new instance of IteratorFromStringHandle
   * @see {@link org.tensorflow.op.core.IteratorFromStringHandle}
   */
  public IteratorFromStringHandle iteratorFromStringHandle(Operand stringHandle,
      List> outputTypes, IteratorFromStringHandle.Options... options) {
    return IteratorFromStringHandle.create(scope, stringHandle, outputTypes, options);
  }

  /**
   * Adds an {@link ResourceScatterUpdate} operation to the graph
   *
   * @param resource Should be from a `Variable` node.
   * @param indices A tensor of indices into the first dimension of `ref`.
   * @param updates A tensor of updated values to add to `ref`.
   * @return a new instance of ResourceScatterUpdate
   * @see {@link org.tensorflow.op.core.ResourceScatterUpdate}
   */
  public  ResourceScatterUpdate resourceScatterUpdate(Operand resource,
      Operand indices, Operand updates) {
    return ResourceScatterUpdate.create(scope, resource, indices, updates);
  }

  /**
   * Adds an {@link SparseApplyProximalGradientDescent} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param alpha Scaling factor. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 regularization. Must be a scalar.
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param options carries optional attributes values
   * @return a new instance of SparseApplyProximalGradientDescent
   * @see {@link org.tensorflow.op.core.SparseApplyProximalGradientDescent}
   */
  public  SparseApplyProximalGradientDescent sparseApplyProximalGradientDescent(Operand var,
      Operand alpha, Operand l1, Operand l2, Operand grad, Operand indices,
      SparseApplyProximalGradientDescent.Options... options) {
    return SparseApplyProximalGradientDescent.create(scope, var, alpha, l1, l2, grad, indices, options);
  }

  /**
   * Adds an {@link RandomUniform} operation to the graph
   *
   * @param shape The shape of the output tensor.
   * @param dtype The type of the output.
   * @param options carries optional attributes values
   * @return a new instance of RandomUniform
   * @see {@link org.tensorflow.op.core.RandomUniform}
   */
  public  RandomUniform randomUniform(Operand shape,
      Class dtype, RandomUniform.Options... options) {
    return RandomUniform.create(scope, shape, dtype, options);
  }

  /**
   * Adds an {@link Add} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of Add
   * @see {@link org.tensorflow.op.core.Add}
   */
  public  Add add(Operand x, Operand y) {
    return Add.create(scope, x, y);
  }

  /**
   * Adds an {@link BatchToSpace} operation to the graph
   *
   * @param input 4-D tensor with shape
   * @param crops 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies
   * @param blockSize 
   * @return a new instance of BatchToSpace
   * @see {@link org.tensorflow.op.core.BatchToSpace}
   */
  public  BatchToSpace batchToSpace(Operand input, Operand crops,
      Long blockSize) {
    return BatchToSpace.create(scope, input, crops, blockSize);
  }

  /**
   * Adds an {@link FloorMod} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of FloorMod
   * @see {@link org.tensorflow.op.core.FloorMod}
   */
  public  FloorMod floorMod(Operand x, Operand y) {
    return FloorMod.create(scope, x, y);
  }

  /**
   * Adds an {@link QuantizedAdd} operation to the graph
   *
   * @param x 
   * @param y 
   * @param minX The float value that the lowest quantized `x` value represents.
   * @param maxX The float value that the highest quantized `x` value represents.
   * @param minY The float value that the lowest quantized `y` value represents.
   * @param maxY The float value that the highest quantized `y` value represents.
   * @param Toutput 
   * @return a new instance of QuantizedAdd
   * @see {@link org.tensorflow.op.core.QuantizedAdd}
   */
  public  QuantizedAdd quantizedAdd(Operand x, Operand y, Operand minX,
      Operand maxX, Operand minY, Operand maxY, Class Toutput) {
    return QuantizedAdd.create(scope, x, y, minX, maxX, minY, maxY, Toutput);
  }

  /**
   * Adds an {@link MatrixTriangularSolve} operation to the graph
   *
   * @param matrix Shape is `[..., M, M]`.
   * @param rhs Shape is `[..., M, K]`.
   * @param options carries optional attributes values
   * @return a new instance of MatrixTriangularSolve
   * @see {@link org.tensorflow.op.core.MatrixTriangularSolve}
   */
  public  MatrixTriangularSolve matrixTriangularSolve(Operand matrix, Operand rhs,
      MatrixTriangularSolve.Options... options) {
    return MatrixTriangularSolve.create(scope, matrix, rhs, options);
  }

  /**
   * Adds an {@link UniqueV2} operation to the graph
   *
   * @param x A `Tensor`.
   * @param axis A `Tensor` of type `int32` (default: None). The axis of the Tensor to
   * @param outIdx 
   * @return a new instance of UniqueV2
   * @see {@link org.tensorflow.op.core.UniqueV2}
   */
  public  UniqueV2 uniqueV2(Operand x,
      Operand axis, Class outIdx) {
    return UniqueV2.create(scope, x, axis, outIdx);
  }

  /**
   * Adds an {@link FusedBatchNorm} operation to the graph
   *
   * @param x A 4D Tensor for input data.
   * @param scale A 1D Tensor for scaling factor, to scale the normalized x.
   * @param offset A 1D Tensor for offset, to shift to the normalized x.
   * @param mean A 1D Tensor for population mean. Used for inference only;
   * @param variance A 1D Tensor for population variance. Used for inference only;
   * @param options carries optional attributes values
   * @return a new instance of FusedBatchNorm
   * @see {@link org.tensorflow.op.core.FusedBatchNorm}
   */
  public  FusedBatchNorm fusedBatchNorm(Operand x, Operand scale,
      Operand offset, Operand mean, Operand variance, FusedBatchNorm.Options... options) {
    return FusedBatchNorm.create(scope, x, scale, offset, mean, variance, options);
  }

  /**
   * Adds an {@link ApplyPowerSign} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param m Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param logbase Must be a scalar.
   * @param signDecay Must be a scalar.
   * @param beta Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ApplyPowerSign
   * @see {@link org.tensorflow.op.core.ApplyPowerSign}
   */
  public  ApplyPowerSign applyPowerSign(Operand var, Operand m, Operand lr,
      Operand logbase, Operand signDecay, Operand beta, Operand grad,
      ApplyPowerSign.Options... options) {
    return ApplyPowerSign.create(scope, var, m, lr, logbase, signDecay, beta, grad, options);
  }

  /**
   * Adds an {@link FakeQuantWithMinMaxVars} operation to the graph
   *
   * @param inputs 
   * @param min 
   * @param max 
   * @param options carries optional attributes values
   * @return a new instance of FakeQuantWithMinMaxVars
   * @see {@link org.tensorflow.op.core.FakeQuantWithMinMaxVars}
   */
  public FakeQuantWithMinMaxVars fakeQuantWithMinMaxVars(Operand inputs, Operand min,
      Operand max, FakeQuantWithMinMaxVars.Options... options) {
    return FakeQuantWithMinMaxVars.create(scope, inputs, min, max, options);
  }

  /**
   * Adds an {@link SparseToSparseSetOperation} operation to the graph
   *
   * @param set1Indices 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
   * @param set1Values 1D `Tensor`, values of a `SparseTensor`. Must be in row-major
   * @param set1Shape 1D `Tensor`, shape of a `SparseTensor`. `set1_shape[0...n-1]` must
   * @param set2Indices 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
   * @param set2Values 1D `Tensor`, values of a `SparseTensor`. Must be in row-major
   * @param set2Shape 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must
   * @param setOperation 
   * @param options carries optional attributes values
   * @return a new instance of SparseToSparseSetOperation
   * @see {@link org.tensorflow.op.core.SparseToSparseSetOperation}
   */
  public  SparseToSparseSetOperation sparseToSparseSetOperation(Operand set1Indices,
      Operand set1Values, Operand set1Shape, Operand set2Indices,
      Operand set2Values, Operand set2Shape, String setOperation,
      SparseToSparseSetOperation.Options... options) {
    return SparseToSparseSetOperation.create(scope, set1Indices, set1Values, set1Shape, set2Indices, set2Values, set2Shape, setOperation, options);
  }

  /**
   * Adds an {@link SparseAccumulatorApplyGradient} operation to the graph
   *
   * @param handle The handle to a accumulator.
   * @param localStep The local_step value at which the sparse gradient was computed.
   * @param gradientIndices Indices of the sparse gradient to be accumulated. Must be a
   * @param gradientValues Values are the non-zero slices of the gradient, and must have
   * @param gradientShape Shape of the sparse gradient to be accumulated.
   * @param hasKnownShape Boolean indicating whether gradient_shape is unknown, in which
   * @return a new instance of SparseAccumulatorApplyGradient
   * @see {@link org.tensorflow.op.core.SparseAccumulatorApplyGradient}
   */
  public  SparseAccumulatorApplyGradient sparseAccumulatorApplyGradient(Operand handle,
      Operand localStep, Operand gradientIndices, Operand gradientValues,
      Operand gradientShape, Boolean hasKnownShape) {
    return SparseAccumulatorApplyGradient.create(scope, handle, localStep, gradientIndices, gradientValues, gradientShape, hasKnownShape);
  }

  /**
   * Adds an {@link WholeFileReader} operation to the graph
   *
   * @param options carries optional attributes values
   * @return a new instance of WholeFileReader
   * @see {@link org.tensorflow.op.core.WholeFileReader}
   */
  public WholeFileReader wholeFileReader(WholeFileReader.Options... options) {
    return WholeFileReader.create(scope, options);
  }

  /**
   * Adds an {@link TopK} operation to the graph
   *
   * @param input 1-D or higher with last dimension at least `k`.
   * @param k 0-D.  Number of top elements to look for along the last dimension (along each
   * @param options carries optional attributes values
   * @return a new instance of TopK
   * @see {@link org.tensorflow.op.core.TopK}
   */
  public  TopK topK(Operand input, Operand k,
      TopK.Options... options) {
    return TopK.create(scope, input, k, options);
  }

  /**
   * Adds an {@link SparseToDense} operation to the graph
   *
   * @param sparseIndices 0-D, 1-D, or 2-D.  `sparse_indices[i]` contains the complete
   * @param outputShape 1-D.  Shape of the dense output tensor.
   * @param sparseValues 1-D.  Values corresponding to each row of `sparse_indices`,
   * @param defaultValue Scalar value to set for indices not specified in
   * @param options carries optional attributes values
   * @return a new instance of SparseToDense
   * @see {@link org.tensorflow.op.core.SparseToDense}
   */
  public  SparseToDense sparseToDense(Operand sparseIndices,
      Operand outputShape, Operand sparseValues, Operand defaultValue,
      SparseToDense.Options... options) {
    return SparseToDense.create(scope, sparseIndices, outputShape, sparseValues, defaultValue, options);
  }

  /**
   * Adds an {@link SparseSoftmax} operation to the graph
   *
   * @param spIndices 2-D.  `NNZ x R` matrix with the indices of non-empty values in a
   * @param spValues 1-D.  `NNZ` non-empty values corresponding to `sp_indices`.
   * @param spShape 1-D.  Shape of the input SparseTensor.
   * @return a new instance of SparseSoftmax
   * @see {@link org.tensorflow.op.core.SparseSoftmax}
   */
  public  SparseSoftmax sparseSoftmax(Operand spIndices,
      Operand spValues, Operand spShape) {
    return SparseSoftmax.create(scope, spIndices, spValues, spShape);
  }

  /**
   * Adds an {@link Mul} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of Mul
   * @see {@link org.tensorflow.op.core.Mul}
   */
  public  Mul mul(Operand x, Operand y) {
    return Mul.create(scope, x, y);
  }

  /**
   * Adds an {@link MatrixInverse} operation to the graph
   *
   * @param input Shape is `[..., M, M]`.
   * @param options carries optional attributes values
   * @return a new instance of MatrixInverse
   * @see {@link org.tensorflow.op.core.MatrixInverse}
   */
  public  MatrixInverse matrixInverse(Operand input, MatrixInverse.Options... options) {
    return MatrixInverse.create(scope, input, options);
  }

  /**
   * Adds an {@link Cumsum} operation to the graph
   *
   * @param x A `Tensor`. Must be one of the following types: `float32`, `float64`,
   * @param axis A `Tensor` of type `int32` (default: 0). Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of Cumsum
   * @see {@link org.tensorflow.op.core.Cumsum}
   */
  public  Cumsum cumsum(Operand x, Operand axis,
      Cumsum.Options... options) {
    return Cumsum.create(scope, x, axis, options);
  }

  /**
   * Adds an {@link Qr} operation to the graph
   *
   * @param input A tensor of shape `[..., M, N]` whose inner-most 2 dimensions
   * @param options carries optional attributes values
   * @return a new instance of Qr
   * @see {@link org.tensorflow.op.core.Qr}
   */
  public  Qr qr(Operand input, Qr.Options... options) {
    return Qr.create(scope, input, options);
  }

  /**
   * Adds an {@link ShardedFilespec} operation to the graph
   *
   * @param basename 
   * @param numShards 
   * @return a new instance of ShardedFilespec
   * @see {@link org.tensorflow.op.core.ShardedFilespec}
   */
  public ShardedFilespec shardedFilespec(Operand basename, Operand numShards) {
    return ShardedFilespec.create(scope, basename, numShards);
  }

  /**
   * Adds an {@link FractionalAvgPool} operation to the graph
   *
   * @param value 4-D with shape `[batch, height, width, channels]`.
   * @param poolingRatio Pooling ratio for each dimension of `value`, currently only
   * @param options carries optional attributes values
   * @return a new instance of FractionalAvgPool
   * @see {@link org.tensorflow.op.core.FractionalAvgPool}
   */
  public  FractionalAvgPool fractionalAvgPool(Operand value,
      List poolingRatio, FractionalAvgPool.Options... options) {
    return FractionalAvgPool.create(scope, value, poolingRatio, options);
  }

  /**
   * Adds an {@link CheckNumerics} operation to the graph
   *
   * @param tensor 
   * @param message Prefix of the error message.
   * @return a new instance of CheckNumerics
   * @see {@link org.tensorflow.op.core.CheckNumerics}
   */
  public  CheckNumerics checkNumerics(Operand tensor, String message) {
    return CheckNumerics.create(scope, tensor, message);
  }

  /**
   * Adds an {@link AvgPool3D} operation to the graph
   *
   * @param input Shape `[batch, depth, rows, cols, channels]` tensor to pool over.
   * @param ksize 1-D tensor of length 5. The size of the window for each dimension of
   * @param strides 1-D tensor of length 5. The stride of the sliding window for each
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of AvgPool3D
   * @see {@link org.tensorflow.op.core.AvgPool3D}
   */
  public  AvgPool3D avgPool3D(Operand input, List ksize,
      List strides, String padding, AvgPool3D.Options... options) {
    return AvgPool3D.create(scope, input, ksize, strides, padding, options);
  }

  /**
   * Adds an {@link Elu} operation to the graph
   *
   * @param features 
   * @return a new instance of Elu
   * @see {@link org.tensorflow.op.core.Elu}
   */
  public  Elu elu(Operand features) {
    return Elu.create(scope, features);
  }

  /**
   * Adds an {@link QuantizedResizeBilinear} operation to the graph
   *
   * @param images 4-D with shape `[batch, height, width, channels]`.
   * @param size = A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
   * @param min 
   * @param max 
   * @param options carries optional attributes values
   * @return a new instance of QuantizedResizeBilinear
   * @see {@link org.tensorflow.op.core.QuantizedResizeBilinear}
   */
  public  QuantizedResizeBilinear quantizedResizeBilinear(Operand images,
      Operand size, Operand min, Operand max,
      QuantizedResizeBilinear.Options... options) {
    return QuantizedResizeBilinear.create(scope, images, size, min, max, options);
  }

  /**
   * Adds an {@link BytesProducedStatsDataset} operation to the graph
   *
   * @param inputDataset 
   * @param tag 
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of BytesProducedStatsDataset
   * @see {@link org.tensorflow.op.core.BytesProducedStatsDataset}
   */
  public BytesProducedStatsDataset bytesProducedStatsDataset(Operand inputDataset,
      Operand tag, List> outputTypes, List outputShapes) {
    return BytesProducedStatsDataset.create(scope, inputDataset, tag, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link Concat} operation to the graph
   *
   * @param values List of `N` Tensors to concatenate. Their ranks and types must match,
   * @param axis 0-D.  The dimension along which to concatenate.  Must be in the
   * @return a new instance of Concat
   * @see {@link org.tensorflow.op.core.Concat}
   */
  public  Concat concat(Operand values, Operand axis) {
    return Concat.create(scope, values, axis);
  }

  /**
   * Adds an {@link FloorDiv} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of FloorDiv
   * @see {@link org.tensorflow.op.core.FloorDiv}
   */
  public  FloorDiv floorDiv(Operand x, Operand y) {
    return FloorDiv.create(scope, x, y);
  }

  /**
   * Adds an {@link QueueDequeue} operation to the graph
   *
   * @param handle The handle to a queue.
   * @param componentTypes The type of each component in a tuple.
   * @param options carries optional attributes values
   * @return a new instance of QueueDequeue
   * @see {@link org.tensorflow.op.core.QueueDequeue}
   */
  public QueueDequeue queueDequeue(Operand handle, List> componentTypes,
      QueueDequeue.Options... options) {
    return QueueDequeue.create(scope, handle, componentTypes, options);
  }

  /**
   * Adds an {@link Constant} operation to the graph
   *
   * @param object a Java object representing the constant.
   * @see org.tensorflow.Tensor#create(Object) Tensor.create
   * @see {@link org.tensorflow.op.core.Constant}
   */
  public  Constant constant(Object object, Class type) {
    return Constant.create(scope, object, type);
  }

  /**
   * Adds an {@link Atanh} operation to the graph
   *
   * @param x 
   * @return a new instance of Atanh
   * @see {@link org.tensorflow.op.core.Atanh}
   */
  public  Atanh atanh(Operand x) {
    return Atanh.create(scope, x);
  }

  /**
   * Adds an {@link BarrierInsertMany} operation to the graph
   *
   * @param handle The handle to a barrier.
   * @param keys A one-dimensional tensor of keys, with length n.
   * @param values An any-dimensional tensor of values, which are associated with the
   * @param componentIndex The component of the barrier elements that is being assigned.
   * @return a new instance of BarrierInsertMany
   * @see {@link org.tensorflow.op.core.BarrierInsertMany}
   */
  public  BarrierInsertMany barrierInsertMany(Operand handle, Operand keys,
      Operand values, Long componentIndex) {
    return BarrierInsertMany.create(scope, handle, keys, values, componentIndex);
  }

  /**
   * Adds an {@link EncodeBase64} operation to the graph
   *
   * @param input Strings to be encoded.
   * @param options carries optional attributes values
   * @return a new instance of EncodeBase64
   * @see {@link org.tensorflow.op.core.EncodeBase64}
   */
  public EncodeBase64 encodeBase64(Operand input, EncodeBase64.Options... options) {
    return EncodeBase64.create(scope, input, options);
  }

  /**
   * Adds an {@link AddSparseToTensorsMap} operation to the graph
   *
   * @param sparseIndices 2-D.  The `indices` of the `SparseTensor`.
   * @param sparseValues 1-D.  The `values` of the `SparseTensor`.
   * @param sparseShape 1-D.  The `shape` of the `SparseTensor`.
   * @param options carries optional attributes values
   * @return a new instance of AddSparseToTensorsMap
   * @see {@link org.tensorflow.op.core.AddSparseToTensorsMap}
   */
  public  AddSparseToTensorsMap addSparseToTensorsMap(Operand sparseIndices,
      Operand sparseValues, Operand sparseShape,
      AddSparseToTensorsMap.Options... options) {
    return AddSparseToTensorsMap.create(scope, sparseIndices, sparseValues, sparseShape, options);
  }

  /**
   * Adds an {@link BatchIFFT} operation to the graph
   *
   * @param input 
   * @return a new instance of BatchIFFT
   * @see {@link org.tensorflow.op.core.BatchIFFT}
   */
  public BatchIFFT batchIFFT(Operand input) {
    return BatchIFFT.create(scope, input);
  }

  /**
   * Adds an {@link IsFinite} operation to the graph
   *
   * @param x 
   * @return a new instance of IsFinite
   * @see {@link org.tensorflow.op.core.IsFinite}
   */
  public  IsFinite isFinite(Operand x) {
    return IsFinite.create(scope, x);
  }

  /**
   * Adds an {@link MaxPool3DGradGrad} operation to the graph
   *
   * @param origInput The original input tensor.
   * @param origOutput The original output tensor.
   * @param grad Output backprop of shape `[batch, depth, rows, cols, channels]`.
   * @param ksize 1-D tensor of length 5. The size of the window for each dimension of
   * @param strides 1-D tensor of length 5. The stride of the sliding window for each
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of MaxPool3DGradGrad
   * @see {@link org.tensorflow.op.core.MaxPool3DGradGrad}
   */
  public  MaxPool3DGradGrad maxPool3DGradGrad(Operand origInput,
      Operand origOutput, Operand grad, List ksize, List strides, String padding,
      MaxPool3DGradGrad.Options... options) {
    return MaxPool3DGradGrad.create(scope, origInput, origOutput, grad, ksize, strides, padding, options);
  }

  /**
   * Adds an {@link LearnedUnigramCandidateSampler} operation to the graph
   *
   * @param trueClasses A batch_size * num_true matrix, in which each row contains the
   * @param numTrue Number of true labels per context.
   * @param numSampled Number of candidates to randomly sample.
   * @param unique If unique is true, we sample with rejection, so that all sampled
   * @param rangeMax The sampler will sample integers from the interval [0, range_max).
   * @param options carries optional attributes values
   * @return a new instance of LearnedUnigramCandidateSampler
   * @see {@link org.tensorflow.op.core.LearnedUnigramCandidateSampler}
   */
  public LearnedUnigramCandidateSampler learnedUnigramCandidateSampler(Operand trueClasses,
      Long numTrue, Long numSampled, Boolean unique, Long rangeMax,
      LearnedUnigramCandidateSampler.Options... options) {
    return LearnedUnigramCandidateSampler.create(scope, trueClasses, numTrue, numSampled, unique, rangeMax, options);
  }

  /**
   * Adds an {@link Rank} operation to the graph
   *
   * @param input 
   * @return a new instance of Rank
   * @see {@link org.tensorflow.op.core.Rank}
   */
  public  Rank rank(Operand input) {
    return Rank.create(scope, input);
  }

  /**
   * Adds an {@link AdjustSaturation} operation to the graph
   *
   * @param images Images to adjust.  At least 3-D.
   * @param scale A float scale to add to the saturation.
   * @return a new instance of AdjustSaturation
   * @see {@link org.tensorflow.op.core.AdjustSaturation}
   */
  public AdjustSaturation adjustSaturation(Operand images, Operand scale) {
    return AdjustSaturation.create(scope, images, scale);
  }

  /**
   * Adds an {@link InitializeTableFromTextFile} operation to the graph
   *
   * @param tableHandle Handle to a table which will be initialized.
   * @param filename Filename of a vocabulary text file.
   * @param keyIndex Column index in a line to get the table `key` values from.
   * @param valueIndex Column index that represents information of a line to get the table
   * @param options carries optional attributes values
   * @return a new instance of InitializeTableFromTextFile
   * @see {@link org.tensorflow.op.core.InitializeTableFromTextFile}
   */
  public InitializeTableFromTextFile initializeTableFromTextFile(Operand tableHandle,
      Operand filename, Long keyIndex, Long valueIndex,
      InitializeTableFromTextFile.Options... options) {
    return InitializeTableFromTextFile.create(scope, tableHandle, filename, keyIndex, valueIndex, options);
  }

  /**
   * Adds an {@link Reverse} operation to the graph
   *
   * @param tensor Up to 8-D.
   * @param axis 1-D. The indices of the dimensions to reverse. Must be in the range
   * @return a new instance of Reverse
   * @see {@link org.tensorflow.op.core.Reverse}
   */
  public  Reverse reverse(Operand tensor, Operand axis) {
    return Reverse.create(scope, tensor, axis);
  }

  /**
   * Adds an {@link DrawBoundingBoxes} operation to the graph
   *
   * @param images 4-D with shape `[batch, height, width, depth]`. A batch of images.
   * @param boxes 3-D with shape `[batch, num_bounding_boxes, 4]` containing bounding
   * @return a new instance of DrawBoundingBoxes
   * @see {@link org.tensorflow.op.core.DrawBoundingBoxes}
   */
  public  DrawBoundingBoxes drawBoundingBoxes(Operand images,
      Operand boxes) {
    return DrawBoundingBoxes.create(scope, images, boxes);
  }

  /**
   * Adds an {@link Real} operation to the graph
   *
   * @param input 
   * @param Tout 
   * @return a new instance of Real
   * @see {@link org.tensorflow.op.core.Real}
   */
  public  Real real(Operand input, Class Tout) {
    return Real.create(scope, input, Tout);
  }

  /**
   * Adds an {@link LookupTableFind} operation to the graph
   *
   * @param tableHandle Handle to the table.
   * @param keys Any shape.  Keys to look up.
   * @param defaultValue 
   * @return a new instance of LookupTableFind
   * @see {@link org.tensorflow.op.core.LookupTableFind}
   */
  public  LookupTableFind lookupTableFind(Operand tableHandle, Operand keys,
      Operand defaultValue) {
    return LookupTableFind.create(scope, tableHandle, keys, defaultValue);
  }

  /**
   * Adds an {@link DecodeRaw} operation to the graph
   *
   * @param bytes All the elements must have the same length.
   * @param outType 
   * @param options carries optional attributes values
   * @return a new instance of DecodeRaw
   * @see {@link org.tensorflow.op.core.DecodeRaw}
   */
  public  DecodeRaw decodeRaw(Operand bytes, Class outType,
      DecodeRaw.Options... options) {
    return DecodeRaw.create(scope, bytes, outType, options);
  }

  /**
   * Adds an {@link TFRecordDataset} operation to the graph
   *
   * @param filenames A scalar or vector containing the name(s) of the file(s) to be
   * @param compressionType A scalar containing either (i) the empty string (no
   * @param bufferSize A scalar representing the number of bytes to buffer. A value of
   * @return a new instance of TFRecordDataset
   * @see {@link org.tensorflow.op.core.TFRecordDataset}
   */
  public TFRecordDataset tFRecordDataset(Operand filenames, Operand compressionType,
      Operand bufferSize) {
    return TFRecordDataset.create(scope, filenames, compressionType, bufferSize);
  }

  /**
   * Adds an {@link DepthwiseConv2dNative} operation to the graph
   *
   * @param input 
   * @param filter 
   * @param strides 1-D of length 4.  The stride of the sliding window for each dimension
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of DepthwiseConv2dNative
   * @see {@link org.tensorflow.op.core.DepthwiseConv2dNative}
   */
  public  DepthwiseConv2dNative depthwiseConv2dNative(Operand input,
      Operand filter, List strides, String padding,
      DepthwiseConv2dNative.Options... options) {
    return DepthwiseConv2dNative.create(scope, input, filter, strides, padding, options);
  }

  /**
   * Adds an {@link SpaceToBatch} operation to the graph
   *
   * @param input 4-D with shape `[batch, height, width, depth]`.
   * @param paddings 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies
   * @param blockSize 
   * @return a new instance of SpaceToBatch
   * @see {@link org.tensorflow.op.core.SpaceToBatch}
   */
  public  SpaceToBatch spaceToBatch(Operand input, Operand paddings,
      Long blockSize) {
    return SpaceToBatch.create(scope, input, paddings, blockSize);
  }

  /**
   * Adds an {@link MaxPoolGradGradWithArgmax} operation to the graph
   *
   * @param input The original input.
   * @param grad 4-D with shape `[batch, height, width, channels]`.  Gradients w.r.t. the
   * @param argmax The indices of the maximum values chosen for each output of `max_pool`.
   * @param ksize The size of the window for each dimension of the input tensor.
   * @param strides The stride of the sliding window for each dimension of the
   * @param padding The type of padding algorithm to use.
   * @return a new instance of MaxPoolGradGradWithArgmax
   * @see {@link org.tensorflow.op.core.MaxPoolGradGradWithArgmax}
   */
  public  MaxPoolGradGradWithArgmax maxPoolGradGradWithArgmax(Operand input,
      Operand grad, Operand argmax, List ksize, List strides, String padding) {
    return MaxPoolGradGradWithArgmax.create(scope, input, grad, argmax, ksize, strides, padding);
  }

  /**
   * Adds an {@link SparseSoftmaxCrossEntropyWithLogits} operation to the graph
   *
   * @param features batch_size x num_classes matrix
   * @param labels batch_size vector with values in [0, num_classes).
   * @return a new instance of SparseSoftmaxCrossEntropyWithLogits
   * @see {@link org.tensorflow.op.core.SparseSoftmaxCrossEntropyWithLogits}
   */
  public  SparseSoftmaxCrossEntropyWithLogits sparseSoftmaxCrossEntropyWithLogits(Operand features,
      Operand labels) {
    return SparseSoftmaxCrossEntropyWithLogits.create(scope, features, labels);
  }

  /**
   * Adds an {@link Ceil} operation to the graph
   *
   * @param x 
   * @return a new instance of Ceil
   * @see {@link org.tensorflow.op.core.Ceil}
   */
  public  Ceil ceil(Operand x) {
    return Ceil.create(scope, x);
  }

  /**
   * Adds an {@link ReaderRestoreState} operation to the graph
   *
   * @param readerHandle Handle to a Reader.
   * @param state Result of a ReaderSerializeState of a Reader with type
   * @return a new instance of ReaderRestoreState
   * @see {@link org.tensorflow.op.core.ReaderRestoreState}
   */
  public ReaderRestoreState readerRestoreState(Operand readerHandle, Operand state) {
    return ReaderRestoreState.create(scope, readerHandle, state);
  }

  /**
   * Adds an {@link Multiply} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of Multiply
   * @see {@link org.tensorflow.op.core.Multiply}
   */
  public  Multiply multiply(Operand x, Operand y) {
    return Multiply.create(scope, x, y);
  }

  /**
   * Adds an {@link DecodeWav} operation to the graph
   *
   * @param contents The WAV-encoded audio, usually from a file.
   * @param options carries optional attributes values
   * @return a new instance of DecodeWav
   * @see {@link org.tensorflow.op.core.DecodeWav}
   */
  public DecodeWav decodeWav(Operand contents, DecodeWav.Options... options) {
    return DecodeWav.create(scope, contents, options);
  }

  /**
   * Adds an {@link Batch} operation to the graph
   *
   * @param inTensors 
   * @param numBatchThreads 
   * @param maxBatchSize 
   * @param batchTimeoutMicros 
   * @param gradTimeoutMicros 
   * @param options carries optional attributes values
   * @return a new instance of Batch
   * @see {@link org.tensorflow.op.core.Batch}
   */
  public Batch batch(Iterable> inTensors, Long numBatchThreads, Long maxBatchSize,
      Long batchTimeoutMicros, Long gradTimeoutMicros, Batch.Options... options) {
    return Batch.create(scope, inTensors, numBatchThreads, maxBatchSize, batchTimeoutMicros, gradTimeoutMicros, options);
  }

  /**
   * Adds an {@link MutexV2} operation to the graph
   *
   * @param options carries optional attributes values
   * @return a new instance of MutexV2
   * @see {@link org.tensorflow.op.core.MutexV2}
   */
  public MutexV2 mutexV2(MutexV2.Options... options) {
    return MutexV2.create(scope, options);
  }

  /**
   * Adds an {@link SparseSegmentMeanGrad} operation to the graph
   *
   * @param grad gradient propagated to the SparseSegmentMean op.
   * @param indices indices passed to the corresponding SparseSegmentMean op.
   * @param segmentIds segment_ids passed to the corresponding SparseSegmentMean op.
   * @param outputDim0 dimension 0 of "data" passed to SparseSegmentMean op.
   * @return a new instance of SparseSegmentMeanGrad
   * @see {@link org.tensorflow.op.core.SparseSegmentMeanGrad}
   */
  public  SparseSegmentMeanGrad sparseSegmentMeanGrad(Operand grad,
      Operand indices, Operand segmentIds, Operand outputDim0) {
    return SparseSegmentMeanGrad.create(scope, grad, indices, segmentIds, outputDim0);
  }

  /**
   * Adds an {@link SparseSparseMaximum} operation to the graph
   *
   * @param aIndices 2-D.  `N x R` matrix with the indices of non-empty values in a
   * @param aValues 1-D.  `N` non-empty values corresponding to `a_indices`.
   * @param aShape 1-D.  Shape of the input SparseTensor.
   * @param bIndices counterpart to `a_indices` for the other operand.
   * @param bValues counterpart to `a_values` for the other operand; must be of the same dtype.
   * @param bShape counterpart to `a_shape` for the other operand; the two shapes must be equal.
   * @return a new instance of SparseSparseMaximum
   * @see {@link org.tensorflow.op.core.SparseSparseMaximum}
   */
  public  SparseSparseMaximum sparseSparseMaximum(Operand aIndices,
      Operand aValues, Operand aShape, Operand bIndices, Operand bValues,
      Operand bShape) {
    return SparseSparseMaximum.create(scope, aIndices, aValues, aShape, bIndices, bValues, bShape);
  }

  /**
   * Adds an {@link QuantizedReluX} operation to the graph
   *
   * @param features 
   * @param maxValue 
   * @param minFeatures The float value that the lowest quantized value represents.
   * @param maxFeatures The float value that the highest quantized value represents.
   * @param outType 
   * @return a new instance of QuantizedReluX
   * @see {@link org.tensorflow.op.core.QuantizedReluX}
   */
  public  QuantizedReluX quantizedReluX(Operand features, Operand maxValue,
      Operand minFeatures, Operand maxFeatures, Class outType) {
    return QuantizedReluX.create(scope, features, maxValue, minFeatures, maxFeatures, outType);
  }

  /**
   * Adds an {@link SqlDataset} operation to the graph
   *
   * @param driverName The database type. Currently, the only supported type is 'sqlite'.
   * @param dataSourceName A connection string to connect to the database.
   * @param query A SQL query to execute.
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of SqlDataset
   * @see {@link org.tensorflow.op.core.SqlDataset}
   */
  public SqlDataset sqlDataset(Operand driverName, Operand dataSourceName,
      Operand query, List> outputTypes, List outputShapes) {
    return SqlDataset.create(scope, driverName, dataSourceName, query, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link RandomShuffleQueue} operation to the graph
   *
   * @param componentTypes The type of each component in a value.
   * @param options carries optional attributes values
   * @return a new instance of RandomShuffleQueue
   * @see {@link org.tensorflow.op.core.RandomShuffleQueue}
   */
  public RandomShuffleQueue randomShuffleQueue(List> componentTypes,
      RandomShuffleQueue.Options... options) {
    return RandomShuffleQueue.create(scope, componentTypes, options);
  }

  /**
   * Adds an {@link Size} operation to the graph
   *
   * @param input 
   * @param outType 
   * @return a new instance of Size
   * @see {@link org.tensorflow.op.core.Size}
   */
  public  Size size(Operand input, Class outType) {
    return Size.create(scope, input, outType);
  }

  /**
   * Adds an {@link TruncateMod} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of TruncateMod
   * @see {@link org.tensorflow.op.core.TruncateMod}
   */
  public  TruncateMod truncateMod(Operand x, Operand y) {
    return TruncateMod.create(scope, x, y);
  }

  /**
   * Adds an {@link BatchIFFT2D} operation to the graph
   *
   * @param input 
   * @return a new instance of BatchIFFT2D
   * @see {@link org.tensorflow.op.core.BatchIFFT2D}
   */
  public BatchIFFT2D batchIFFT2D(Operand input) {
    return BatchIFFT2D.create(scope, input);
  }

  /**
   * Adds an {@link Mod} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of Mod
   * @see {@link org.tensorflow.op.core.Mod}
   */
  public  Mod mod(Operand x, Operand y) {
    return Mod.create(scope, x, y);
  }

  /**
   * Adds an {@link EmptyTensorList} operation to the graph
   *
   * @param elementShape 
   * @param elementDtype 
   * @return a new instance of EmptyTensorList
   * @see {@link org.tensorflow.op.core.EmptyTensorList}
   */
  public  EmptyTensorList emptyTensorList(Operand elementShape,
      Class elementDtype) {
    return EmptyTensorList.create(scope, elementShape, elementDtype);
  }

  /**
   * Adds an {@link LatencyStatsDataset} operation to the graph
   *
   * @param inputDataset 
   * @param tag 
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of LatencyStatsDataset
   * @see {@link org.tensorflow.op.core.LatencyStatsDataset}
   */
  public LatencyStatsDataset latencyStatsDataset(Operand inputDataset, Operand tag,
      List> outputTypes, List outputShapes) {
    return LatencyStatsDataset.create(scope, inputDataset, tag, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link TruncateDiv} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of TruncateDiv
   * @see {@link org.tensorflow.op.core.TruncateDiv}
   */
  public  TruncateDiv truncateDiv(Operand x, Operand y) {
    return TruncateDiv.create(scope, x, y);
  }

  /**
   * Adds an {@link ResourceSparseApplyFtrlV2} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param linear Should be from a Variable().
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param lr Scaling factor. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 shrinkage regulariation. Must be a scalar.
   * @param l2Shrinkage 
   * @param lrPower Scaling factor. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of ResourceSparseApplyFtrlV2
   * @see {@link org.tensorflow.op.core.ResourceSparseApplyFtrlV2}
   */
  public  ResourceSparseApplyFtrlV2 resourceSparseApplyFtrlV2(Operand var,
      Operand accum, Operand linear, Operand grad, Operand indices, Operand lr,
      Operand l1, Operand l2, Operand l2Shrinkage, Operand lrPower,
      ResourceSparseApplyFtrlV2.Options... options) {
    return ResourceSparseApplyFtrlV2.create(scope, var, accum, linear, grad, indices, lr, l1, l2, l2Shrinkage, lrPower, options);
  }

  /**
   * Adds an {@link PrependFromQueueAndPaddedBatchDataset} operation to the graph
   *
   * @param inputDataset 
   * @param batchSize 
   * @param paddedShapes 
   * @param paddingValues 
   * @param outputShapes 
   * @return a new instance of PrependFromQueueAndPaddedBatchDataset
   * @see {@link org.tensorflow.op.core.PrependFromQueueAndPaddedBatchDataset}
   */
  public PrependFromQueueAndPaddedBatchDataset prependFromQueueAndPaddedBatchDataset(Operand inputDataset,
      Operand batchSize, Iterable> paddedShapes,
      Iterable> paddingValues, List outputShapes) {
    return PrependFromQueueAndPaddedBatchDataset.create(scope, inputDataset, batchSize, paddedShapes, paddingValues, outputShapes);
  }

  /**
   * Adds an {@link Pow} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of Pow
   * @see {@link org.tensorflow.op.core.Pow}
   */
  public  Pow pow(Operand x, Operand y) {
    return Pow.create(scope, x, y);
  }

  /**
   * Adds an {@link StatelessRandomUniform} operation to the graph
   *
   * @param shape The shape of the output tensor.
   * @param seed 2 seeds (shape [2]).
   * @param dtype The type of the output.
   * @return a new instance of StatelessRandomUniform
   * @see {@link org.tensorflow.op.core.StatelessRandomUniform}
   */
  public  StatelessRandomUniform statelessRandomUniform(Operand shape,
      Operand seed, Class dtype) {
    return StatelessRandomUniform.create(scope, shape, seed, dtype);
  }

  /**
   * Adds an {@link ResourceScatterNdUpdate} operation to the graph
   *
   * @param ref A resource handle. Must be from a VarHandleOp.
   * @param indices A Tensor. Must be one of the following types: int32, int64.
   * @param updates A Tensor. Must have the same type as ref. A tensor of updated
   * @param options carries optional attributes values
   * @return a new instance of ResourceScatterNdUpdate
   * @see {@link org.tensorflow.op.core.ResourceScatterNdUpdate}
   */
  public  ResourceScatterNdUpdate resourceScatterNdUpdate(Operand ref,
      Operand indices, Operand updates, ResourceScatterNdUpdate.Options... options) {
    return ResourceScatterNdUpdate.create(scope, ref, indices, updates, options);
  }

  /**
   * Adds an {@link BatchDataset} operation to the graph
   *
   * @param inputDataset 
   * @param batchSize A scalar representing the number of elements to accumulate in a
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of BatchDataset
   * @see {@link org.tensorflow.op.core.BatchDataset}
   */
  public BatchDataset batchDataset(Operand inputDataset, Operand batchSize,
      List> outputTypes, List outputShapes) {
    return BatchDataset.create(scope, inputDataset, batchSize, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link Mean} operation to the graph
   *
   * @param input The tensor to reduce.
   * @param axis The dimensions to reduce. Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of Mean
   * @see {@link org.tensorflow.op.core.Mean}
   */
  public  Mean mean(Operand input, Operand axis,
      Mean.Options... options) {
    return Mean.create(scope, input, axis, options);
  }

  /**
   * Adds an {@link UnsortedSegmentSum} operation to the graph
   *
   * @param data 
   * @param segmentIds A tensor whose shape is a prefix of `data.shape`.
   * @param numSegments 
   * @return a new instance of UnsortedSegmentSum
   * @see {@link org.tensorflow.op.core.UnsortedSegmentSum}
   */
  public  UnsortedSegmentSum unsortedSegmentSum(Operand data,
      Operand segmentIds, Operand numSegments) {
    return UnsortedSegmentSum.create(scope, data, segmentIds, numSegments);
  }

  /**
   * Adds an {@link AssignSubVariableOp} operation to the graph
   *
   * @param resource handle to the resource in which to store the variable.
   * @param value the value by which the variable will be incremented.
   * @return a new instance of AssignSubVariableOp
   * @see {@link org.tensorflow.op.core.AssignSubVariableOp}
   */
  public  AssignSubVariableOp assignSubVariableOp(Operand resource, Operand value) {
    return AssignSubVariableOp.create(scope, resource, value);
  }

  /**
   * Adds an {@link FFT3D} operation to the graph
   *
   * @param input A complex64 tensor.
   * @return a new instance of FFT3D
   * @see {@link org.tensorflow.op.core.FFT3D}
   */
  public  FFT3D fFT3D(Operand input) {
    return FFT3D.create(scope, input);
  }

  /**
   * Adds an {@link CholeskyGrad} operation to the graph
   *
   * @param l Output of batch Cholesky algorithm l = cholesky(A). Shape is `[..., M, M]`.
   * @param grad df/dl where f is some scalar function. Shape is `[..., M, M]`.
   * @return a new instance of CholeskyGrad
   * @see {@link org.tensorflow.op.core.CholeskyGrad}
   */
  public  CholeskyGrad choleskyGrad(Operand l, Operand grad) {
    return CholeskyGrad.create(scope, l, grad);
  }

  /**
   * Adds an {@link ReduceMean} operation to the graph
   *
   * @param input The tensor to reduce.
   * @param axis The dimensions to reduce. Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of ReduceMean
   * @see {@link org.tensorflow.op.core.ReduceMean}
   */
  public  ReduceMean reduceMean(Operand input, Operand axis,
      ReduceMean.Options... options) {
    return ReduceMean.create(scope, input, axis, options);
  }

  /**
   * Adds an {@link DecodePng} operation to the graph
   *
   * @param contents 0-D.  The PNG-encoded image.
   * @param dtype 
   * @param options carries optional attributes values
   * @return a new instance of DecodePng
   * @see {@link org.tensorflow.op.core.DecodePng}
   */
  public  DecodePng decodePng(Operand contents, Class dtype,
      DecodePng.Options... options) {
    return DecodePng.create(scope, contents, dtype, options);
  }

  /**
   * Adds an {@link Tan} operation to the graph
   *
   * @param x 
   * @return a new instance of Tan
   * @see {@link org.tensorflow.op.core.Tan}
   */
  public  Tan tan(Operand x) {
    return Tan.create(scope, x);
  }

  /**
   * Adds an {@link Div} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of Div
   * @see {@link org.tensorflow.op.core.Div}
   */
  public  Div div(Operand x, Operand y) {
    return Div.create(scope, x, y);
  }

  /**
   * Adds an {@link AssignVariableOp} operation to the graph
   *
   * @param resource handle to the resource in which to store the variable.
   * @param value the value to set the new tensor to use.
   * @return a new instance of AssignVariableOp
   * @see {@link org.tensorflow.op.core.AssignVariableOp}
   */
  public  AssignVariableOp assignVariableOp(Operand resource, Operand value) {
    return AssignVariableOp.create(scope, resource, value);
  }

  /**
   * Adds an {@link EagerPyFunc} operation to the graph
   *
   * @param input 
   * @param token 
   * @param Tout 
   * @return a new instance of EagerPyFunc
   * @see {@link org.tensorflow.op.core.EagerPyFunc}
   */
  public EagerPyFunc eagerPyFunc(Iterable> input, String token, List> Tout) {
    return EagerPyFunc.create(scope, input, token, Tout);
  }

  /**
   * Adds an {@link TensorListLength} operation to the graph
   *
   * @param inputHandle 
   * @return a new instance of TensorListLength
   * @see {@link org.tensorflow.op.core.TensorListLength}
   */
  public TensorListLength tensorListLength(Operand inputHandle) {
    return TensorListLength.create(scope, inputHandle);
  }

  /**
   * Adds an {@link Conj} operation to the graph
   *
   * @param input 
   * @return a new instance of Conj
   * @see {@link org.tensorflow.op.core.Conj}
   */
  public  Conj conj(Operand input) {
    return Conj.create(scope, input);
  }

  /**
   * Adds an {@link QueueEnqueueMany} operation to the graph
   *
   * @param handle The handle to a queue.
   * @param components One or more tensors from which the enqueued tensors should
   * @param options carries optional attributes values
   * @return a new instance of QueueEnqueueMany
   * @see {@link org.tensorflow.op.core.QueueEnqueueMany}
   */
  public QueueEnqueueMany queueEnqueueMany(Operand handle, Iterable> components,
      QueueEnqueueMany.Options... options) {
    return QueueEnqueueMany.create(scope, handle, components, options);
  }

  /**
   * Adds an {@link ReduceSum} operation to the graph
   *
   * @param input The tensor to reduce.
   * @param axis The dimensions to reduce. Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of ReduceSum
   * @see {@link org.tensorflow.op.core.ReduceSum}
   */
  public  ReduceSum reduceSum(Operand input, Operand axis,
      ReduceSum.Options... options) {
    return ReduceSum.create(scope, input, axis, options);
  }

  /**
   * Adds an {@link NegTrain} operation to the graph
   *
   * @param wIn input word embedding.
   * @param wOut output word embedding.
   * @param examples A vector of word ids.
   * @param labels A vector of word ids.
   * @param lr 
   * @param vocabCount Count of words in the vocabulary.
   * @param numNegativeSamples Number of negative samples per example.
   * @return a new instance of NegTrain
   * @see {@link org.tensorflow.op.core.NegTrain}
   */
  public NegTrain negTrain(Operand wIn, Operand wOut, Operand examples,
      Operand labels, Operand lr, List vocabCount, Long numNegativeSamples) {
    return NegTrain.create(scope, wIn, wOut, examples, labels, lr, vocabCount, numNegativeSamples);
  }

  /**
   * Adds an {@link SparseConcat} operation to the graph
   *
   * @param indices 2-D.  Indices of each input `SparseTensor`.
   * @param values 1-D.  Non-empty values of each `SparseTensor`.
   * @param shapes 1-D.  Shapes of each `SparseTensor`.
   * @param concatDim Dimension to concatenate along. Must be in range [-rank, rank),
   * @return a new instance of SparseConcat
   * @see {@link org.tensorflow.op.core.SparseConcat}
   */
  public  SparseConcat sparseConcat(Iterable> indices, Operand values,
      Iterable> shapes, Long concatDim) {
    return SparseConcat.create(scope, indices, values, shapes, concatDim);
  }

  /**
   * Adds an {@link Invert} operation to the graph
   *
   * @param x 
   * @return a new instance of Invert
   * @see {@link org.tensorflow.op.core.Invert}
   */
  public  Invert invert(Operand x) {
    return Invert.create(scope, x);
  }

  /**
   * Adds an {@link SparseSegmentSumWithNumSegments} operation to the graph
   *
   * @param data 
   * @param indices A 1-D tensor. Has same rank as `segment_ids`.
   * @param segmentIds A 1-D tensor. Values should be sorted and can be repeated.
   * @param numSegments Should equal the number of distinct segment IDs.
   * @return a new instance of SparseSegmentSumWithNumSegments
   * @see {@link org.tensorflow.op.core.SparseSegmentSumWithNumSegments}
   */
  public  SparseSegmentSumWithNumSegments sparseSegmentSumWithNumSegments(Operand data,
      Operand indices, Operand segmentIds, Operand numSegments) {
    return SparseSegmentSumWithNumSegments.create(scope, data, indices, segmentIds, numSegments);
  }

  /**
   * Adds an {@link SparseDenseCwiseMul} operation to the graph
   *
   * @param spIndices 2-D.  `N x R` matrix with the indices of non-empty values in a
   * @param spValues 1-D.  `N` non-empty values corresponding to `sp_indices`.
   * @param spShape 1-D.  Shape of the input SparseTensor.
   * @param dense `R`-D.  The dense Tensor operand.
   * @return a new instance of SparseDenseCwiseMul
   * @see {@link org.tensorflow.op.core.SparseDenseCwiseMul}
   */
  public  SparseDenseCwiseMul sparseDenseCwiseMul(Operand spIndices,
      Operand spValues, Operand spShape, Operand dense) {
    return SparseDenseCwiseMul.create(scope, spIndices, spValues, spShape, dense);
  }

  /**
   * Adds an {@link MaxPoolGradGradV2} operation to the graph
   *
   * @param origInput The original input tensor.
   * @param origOutput The original output tensor.
   * @param grad 4-D.  Gradients of gradients w.r.t. the input of `max_pool`.
   * @param ksize The size of the window for each dimension of the input tensor.
   * @param strides The stride of the sliding window for each dimension of the
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of MaxPoolGradGradV2
   * @see {@link org.tensorflow.op.core.MaxPoolGradGradV2}
   */
  public  MaxPoolGradGradV2 maxPoolGradGradV2(Operand origInput,
      Operand origOutput, Operand grad, Operand ksize, Operand strides,
      String padding, MaxPoolGradGradV2.Options... options) {
    return MaxPoolGradGradV2.create(scope, origInput, origOutput, grad, ksize, strides, padding, options);
  }

  /**
   * Adds an {@link ApplyProximalGradientDescent} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param alpha Scaling factor. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 regularization. Must be a scalar.
   * @param delta The change.
   * @param options carries optional attributes values
   * @return a new instance of ApplyProximalGradientDescent
   * @see {@link org.tensorflow.op.core.ApplyProximalGradientDescent}
   */
  public  ApplyProximalGradientDescent applyProximalGradientDescent(Operand var,
      Operand alpha, Operand l1, Operand l2, Operand delta,
      ApplyProximalGradientDescent.Options... options) {
    return ApplyProximalGradientDescent.create(scope, var, alpha, l1, l2, delta, options);
  }

  /**
   * Adds an {@link RGBToHSV} operation to the graph
   *
   * @param images 1-D or higher rank. RGB data to convert. Last dimension must be size 3.
   * @return a new instance of RGBToHSV
   * @see {@link org.tensorflow.op.core.RGBToHSV}
   */
  public  RGBToHSV rGBToHSV(Operand images) {
    return RGBToHSV.create(scope, images);
  }

  /**
   * Adds an {@link Acosh} operation to the graph
   *
   * @param x 
   * @return a new instance of Acosh
   * @see {@link org.tensorflow.op.core.Acosh}
   */
  public  Acosh acosh(Operand x) {
    return Acosh.create(scope, x);
  }

  /**
   * Adds an {@link ApplyAdadelta} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param accumUpdate Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param rho Decay factor. Must be a scalar.
   * @param epsilon Constant factor. Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ApplyAdadelta
   * @see {@link org.tensorflow.op.core.ApplyAdadelta}
   */
  public  ApplyAdadelta applyAdadelta(Operand var, Operand accum,
      Operand accumUpdate, Operand lr, Operand rho, Operand epsilon, Operand grad,
      ApplyAdadelta.Options... options) {
    return ApplyAdadelta.create(scope, var, accum, accumUpdate, lr, rho, epsilon, grad, options);
  }

  /**
   * Adds an {@link Acos} operation to the graph
   *
   * @param x 
   * @return a new instance of Acos
   * @see {@link org.tensorflow.op.core.Acos}
   */
  public  Acos acos(Operand x) {
    return Acos.create(scope, x);
  }

  /**
   * Adds an {@link Conv3DBackpropFilterV2} operation to the graph
   *
   * @param input Shape `[batch, depth, rows, cols, in_channels]`.
   * @param filterSizes An integer vector representing the tensor shape of `filter`,
   * @param outBackprop Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
   * @param strides 1-D tensor of length 5. The stride of the sliding window for each
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of Conv3DBackpropFilterV2
   * @see {@link org.tensorflow.op.core.Conv3DBackpropFilterV2}
   */
  public  Conv3DBackpropFilterV2 conv3DBackpropFilterV2(Operand input,
      Operand filterSizes, Operand outBackprop, List strides, String padding,
      Conv3DBackpropFilterV2.Options... options) {
    return Conv3DBackpropFilterV2.create(scope, input, filterSizes, outBackprop, strides, padding, options);
  }

  /**
   * Adds an {@link BatchFFT} operation to the graph
   *
   * @param input 
   * @return a new instance of BatchFFT
   * @see {@link org.tensorflow.op.core.BatchFFT}
   */
  public BatchFFT batchFFT(Operand input) {
    return BatchFFT.create(scope, input);
  }

  /**
   * Adds an {@link FusedPadConv2D} operation to the graph
   *
   * @param input 4-D with shape `[batch, in_height, in_width, in_channels]`.
   * @param paddings A two-column matrix specifying the padding sizes. The number of
   * @param filter 4-D with shape
   * @param mode 
   * @param strides 1-D of length 4.  The stride of the sliding window for each dimension
   * @param padding The type of padding algorithm to use.
   * @return a new instance of FusedPadConv2D
   * @see {@link org.tensorflow.op.core.FusedPadConv2D}
   */
  public  FusedPadConv2D fusedPadConv2D(Operand input,
      Operand paddings, Operand filter, String mode, List strides,
      String padding) {
    return FusedPadConv2D.create(scope, input, paddings, filter, mode, strides, padding);
  }

  /**
   * Adds an {@link TensorListPopBack} operation to the graph
   *
   * @param inputHandle 
   * @param elementDtype 
   * @return a new instance of TensorListPopBack
   * @see {@link org.tensorflow.op.core.TensorListPopBack}
   */
  public  TensorListPopBack tensorListPopBack(Operand inputHandle, Class elementDtype) {
    return TensorListPopBack.create(scope, inputHandle, elementDtype);
  }

  /**
   * Adds an {@link TensorListConcatLists} operation to the graph
   *
   * @param inputA 
   * @param inputB 
   * @param elementDtype 
   * @return a new instance of TensorListConcatLists
   * @see {@link org.tensorflow.op.core.TensorListConcatLists}
   */
  public  TensorListConcatLists tensorListConcatLists(Operand inputA, Operand inputB,
      Class elementDtype) {
    return TensorListConcatLists.create(scope, inputA, inputB, elementDtype);
  }

  /**
   * Adds an {@link BatchMatMul} operation to the graph
   *
   * @param x 2-D or higher with shape `[..., r_x, c_x]`.
   * @param y 2-D or higher with shape `[..., r_y, c_y]`.
   * @param options carries optional attributes values
   * @return a new instance of BatchMatMul
   * @see {@link org.tensorflow.op.core.BatchMatMul}
   */
  public  BatchMatMul batchMatMul(Operand x, Operand y,
      BatchMatMul.Options... options) {
    return BatchMatMul.create(scope, x, y, options);
  }

  /**
   * Adds an {@link StringSplitV2} operation to the graph
   *
   * @param input `1-D` string `Tensor`, the strings to split.
   * @param sep `0-D` string `Tensor`, the delimiter character.
   * @param options carries optional attributes values
   * @return a new instance of StringSplitV2
   * @see {@link org.tensorflow.op.core.StringSplitV2}
   */
  public StringSplitV2 stringSplitV2(Operand input, Operand sep,
      StringSplitV2.Options... options) {
    return StringSplitV2.create(scope, input, sep, options);
  }

  /**
   * Adds an {@link Mfcc} operation to the graph
   *
   * @param spectrogram Typically produced by the Spectrogram op, with magnitude_squared
   * @param sampleRate How many samples per second the source audio used.
   * @param options carries optional attributes values
   * @return a new instance of Mfcc
   * @see {@link org.tensorflow.op.core.Mfcc}
   */
  public Mfcc mfcc(Operand spectrogram, Operand sampleRate,
      Mfcc.Options... options) {
    return Mfcc.create(scope, spectrogram, sampleRate, options);
  }

  /**
   * Adds an {@link SparseApplyFtrl} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param linear Should be from a Variable().
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param lr Scaling factor. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 regularization. Must be a scalar.
   * @param lrPower Scaling factor. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of SparseApplyFtrl
   * @see {@link org.tensorflow.op.core.SparseApplyFtrl}
   */
  public  SparseApplyFtrl sparseApplyFtrl(Operand var, Operand accum,
      Operand linear, Operand grad, Operand indices, Operand lr, Operand l1,
      Operand l2, Operand lrPower, SparseApplyFtrl.Options... options) {
    return SparseApplyFtrl.create(scope, var, accum, linear, grad, indices, lr, l1, l2, lrPower, options);
  }

  /**
   * Adds an {@link CudnnRNNCanonicalToParams} operation to the graph
   *
   * @param numLayers 
   * @param numUnits 
   * @param inputSize 
   * @param weights 
   * @param biases 
   * @param options carries optional attributes values
   * @return a new instance of CudnnRNNCanonicalToParams
   * @see {@link org.tensorflow.op.core.CudnnRNNCanonicalToParams}
   */
  public  CudnnRNNCanonicalToParams cudnnRNNCanonicalToParams(Operand numLayers,
      Operand numUnits, Operand inputSize, Operand weights,
      Iterable> biases, CudnnRNNCanonicalToParams.Options... options) {
    return CudnnRNNCanonicalToParams.create(scope, numLayers, numUnits, inputSize, weights, biases, options);
  }

  /**
   * Adds an {@link FusedBatchNormGrad} operation to the graph
   *
   * @param yBackprop A 4D Tensor for the gradient with respect to y.
   * @param x A 4D Tensor for input data.
   * @param scale A 1D Tensor for scaling factor, to scale the normalized x.
   * @param reserveSpace1 When is_training is True, a 1D Tensor for the computed batch
   * @param reserveSpace2 When is_training is True, a 1D Tensor for the computed batch
   * @param options carries optional attributes values
   * @return a new instance of FusedBatchNormGrad
   * @see {@link org.tensorflow.op.core.FusedBatchNormGrad}
   */
  public  FusedBatchNormGrad fusedBatchNormGrad(Operand yBackprop,
      Operand x, Operand scale, Operand reserveSpace1, Operand reserveSpace2,
      FusedBatchNormGrad.Options... options) {
    return FusedBatchNormGrad.create(scope, yBackprop, x, scale, reserveSpace1, reserveSpace2, options);
  }

  /**
   * Adds an {@link TensorSummary} operation to the graph
   *
   * @param tensor A tensor to serialize.
   * @param options carries optional attributes values
   * @return a new instance of TensorSummary
   * @see {@link org.tensorflow.op.core.TensorSummary}
   */
  public  TensorSummary tensorSummary(Operand tensor, TensorSummary.Options... options) {
    return TensorSummary.create(scope, tensor, options);
  }

  /**
   * Adds an {@link Transpose} operation to the graph
   *
   * @param x 
   * @param perm 
   * @return a new instance of Transpose
   * @see {@link org.tensorflow.op.core.Transpose}
   */
  public  Transpose transpose(Operand x, Operand perm) {
    return Transpose.create(scope, x, perm);
  }

  /**
   * Adds an {@link Equal} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of Equal
   * @see {@link org.tensorflow.op.core.Equal}
   */
  public  Equal equal(Operand x, Operand y) {
    return Equal.create(scope, x, y);
  }

  /**
   * Adds an {@link NthElement} operation to the graph
   *
   * @param input 1-D or higher with last dimension at least `n+1`.
   * @param n 0-D. Position of sorted vector to select along the last dimension (along
   * @param options carries optional attributes values
   * @return a new instance of NthElement
   * @see {@link org.tensorflow.op.core.NthElement}
   */
  public  NthElement nthElement(Operand input, Operand n,
      NthElement.Options... options) {
    return NthElement.create(scope, input, n, options);
  }

  /**
   * Adds an {@link DecodeGif} operation to the graph
   *
   * @param contents 0-D.  The GIF-encoded image.
   * @return a new instance of DecodeGif
   * @see {@link org.tensorflow.op.core.DecodeGif}
   */
  public DecodeGif decodeGif(Operand contents) {
    return DecodeGif.create(scope, contents);
  }

  /**
   * Adds an {@link Erfc} operation to the graph
   *
   * @param x 
   * @return a new instance of Erfc
   * @see {@link org.tensorflow.op.core.Erfc}
   */
  public  Erfc erfc(Operand x) {
    return Erfc.create(scope, x);
  }

  /**
   * Adds an {@link LessEqual} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of LessEqual
   * @see {@link org.tensorflow.op.core.LessEqual}
   */
  public  LessEqual lessEqual(Operand x, Operand y) {
    return LessEqual.create(scope, x, y);
  }

  /**
   * Adds an {@link SparseCross} operation to the graph
   *
   * @param indices 2-D.  Indices of each input `SparseTensor`.
   * @param values 1-D.   values of each `SparseTensor`.
   * @param shapes 1-D.   Shapes of each `SparseTensor`.
   * @param denseInputs 2-D.    Columns represented by dense `Tensor`.
   * @param hashedOutput If true, returns the hash of the cross instead of the string.
   * @param numBuckets It is used if hashed_output is true.
   * @param hashKey Specify the hash_key that will be used by the `FingerprintCat64`
   * @param outType 
   * @param internalType 
   * @return a new instance of SparseCross
   * @see {@link org.tensorflow.op.core.SparseCross}
   */
  public  SparseCross sparseCross(Iterable> indices,
      Iterable> values, Iterable> shapes, Iterable> denseInputs,
      Boolean hashedOutput, Long numBuckets, Long hashKey, Class outType,
      Class internalType) {
    return SparseCross.create(scope, indices, values, shapes, denseInputs, hashedOutput, numBuckets, hashKey, outType, internalType);
  }

  /**
   * Adds an {@link QuantizedBiasAdd} operation to the graph
   *
   * @param input 
   * @param bias A 1D bias Tensor with size matching the last dimension of 'input'.
   * @param minInput The float value that the lowest quantized input value represents.
   * @param maxInput The float value that the highest quantized input value represents.
   * @param minBias The float value that the lowest quantized bias value represents.
   * @param maxBias The float value that the highest quantized bias value represents.
   * @param outType 
   * @return a new instance of QuantizedBiasAdd
   * @see {@link org.tensorflow.op.core.QuantizedBiasAdd}
   */
  public  QuantizedBiasAdd quantizedBiasAdd(Operand input, Operand bias,
      Operand minInput, Operand maxInput, Operand minBias,
      Operand maxBias, Class outType) {
    return QuantizedBiasAdd.create(scope, input, bias, minInput, maxInput, minBias, maxBias, outType);
  }

  /**
   * Adds an {@link RealDiv} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of RealDiv
   * @see {@link org.tensorflow.op.core.RealDiv}
   */
  public  RealDiv realDiv(Operand x, Operand y) {
    return RealDiv.create(scope, x, y);
  }

  /**
   * Adds an {@link SparseApplyProximalAdagrad} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param lr Learning rate. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 regularization. Must be a scalar.
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param options carries optional attributes values
   * @return a new instance of SparseApplyProximalAdagrad
   * @see {@link org.tensorflow.op.core.SparseApplyProximalAdagrad}
   */
  public  SparseApplyProximalAdagrad sparseApplyProximalAdagrad(Operand var,
      Operand accum, Operand lr, Operand l1, Operand l2, Operand grad,
      Operand indices, SparseApplyProximalAdagrad.Options... options) {
    return SparseApplyProximalAdagrad.create(scope, var, accum, lr, l1, l2, grad, indices, options);
  }

  /**
   * Adds an {@link QueueEnqueue} operation to the graph
   *
   * @param handle The handle to a queue.
   * @param components One or more tensors from which the enqueued tensors should be taken.
   * @param options carries optional attributes values
   * @return a new instance of QueueEnqueue
   * @see {@link org.tensorflow.op.core.QueueEnqueue}
   */
  public QueueEnqueue queueEnqueue(Operand handle, Iterable> components,
      QueueEnqueue.Options... options) {
    return QueueEnqueue.create(scope, handle, components, options);
  }

  /**
   * Adds an {@link ScatterNdNonAliasingAdd} operation to the graph
   *
   * @param input A Tensor.
   * @param indices A Tensor. Must be one of the following types: `int32`, `int64`.
   * @param updates A Tensor. Must have the same type as ref. A tensor of updated values
   * @return a new instance of ScatterNdNonAliasingAdd
   * @see {@link org.tensorflow.op.core.ScatterNdNonAliasingAdd}
   */
  public  ScatterNdNonAliasingAdd scatterNdNonAliasingAdd(Operand input,
      Operand indices, Operand updates) {
    return ScatterNdNonAliasingAdd.create(scope, input, indices, updates);
  }

  /**
   * Adds an {@link IdentityReader} operation to the graph
   *
   * @param options carries optional attributes values
   * @return a new instance of IdentityReader
   * @see {@link org.tensorflow.op.core.IdentityReader}
   */
  public IdentityReader identityReader(IdentityReader.Options... options) {
    return IdentityReader.create(scope, options);
  }

  /**
   * Adds an {@link LogSoftmax} operation to the graph
   *
   * @param logits 2-D with shape `[batch_size, num_classes]`.
   * @return a new instance of LogSoftmax
   * @see {@link org.tensorflow.op.core.LogSoftmax}
   */
  public  LogSoftmax logSoftmax(Operand logits) {
    return LogSoftmax.create(scope, logits);
  }

  /**
   * Adds an {@link AccumulatorTakeGradient} operation to the graph
   *
   * @param handle The handle to an accumulator.
   * @param numRequired Number of gradients required before we return an aggregate.
   * @param dtype The data type of accumulated gradients. Needs to correspond to the type
   * @return a new instance of AccumulatorTakeGradient
   * @see {@link org.tensorflow.op.core.AccumulatorTakeGradient}
   */
  public  AccumulatorTakeGradient accumulatorTakeGradient(Operand handle,
      Operand numRequired, Class dtype) {
    return AccumulatorTakeGradient.create(scope, handle, numRequired, dtype);
  }

  /**
   * Adds an {@link BatchFFT2D} operation to the graph
   *
   * @param input 
   * @return a new instance of BatchFFT2D
   * @see {@link org.tensorflow.op.core.BatchFFT2D}
   */
  public BatchFFT2D batchFFT2D(Operand input) {
    return BatchFFT2D.create(scope, input);
  }

  /**
   * Adds an {@link StringJoin} operation to the graph
   *
   * @param inputs A list of string tensors.  The tensors must all have the same shape,
   * @param options carries optional attributes values
   * @return a new instance of StringJoin
   * @see {@link org.tensorflow.op.core.StringJoin}
   */
  public StringJoin stringJoin(Iterable> inputs, StringJoin.Options... options) {
    return StringJoin.create(scope, inputs, options);
  }

  /**
   * Adds an {@link Conv2DBackpropFilter} operation to the graph
   *
   * @param input 4-D with shape `[batch, in_height, in_width, in_channels]`.
   * @param filterSizes An integer vector representing the tensor shape of `filter`,
   * @param outBackprop 4-D with shape `[batch, out_height, out_width, out_channels]`.
   * @param strides The stride of the sliding window for each dimension of the input
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of Conv2DBackpropFilter
   * @see {@link org.tensorflow.op.core.Conv2DBackpropFilter}
   */
  public  Conv2DBackpropFilter conv2DBackpropFilter(Operand input,
      Operand filterSizes, Operand outBackprop, List strides, String padding,
      Conv2DBackpropFilter.Options... options) {
    return Conv2DBackpropFilter.create(scope, input, filterSizes, outBackprop, strides, padding, options);
  }

  /**
   * Adds an {@link PreventGradient} operation to the graph
   *
   * @param input any tensor.
   * @param options carries optional attributes values
   * @return a new instance of PreventGradient
   * @see {@link org.tensorflow.op.core.PreventGradient}
   */
  public  PreventGradient preventGradient(Operand input,
      PreventGradient.Options... options) {
    return PreventGradient.create(scope, input, options);
  }

  /**
   * Adds an {@link Iterator} operation to the graph
   *
   * @param sharedName 
   * @param container 
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of Iterator
   * @see {@link org.tensorflow.op.core.Iterator}
   */
  public Iterator iterator(String sharedName, String container, List> outputTypes,
      List outputShapes) {
    return Iterator.create(scope, sharedName, container, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link HashTable} operation to the graph
   *
   * @param keyDtype Type of the table keys.
   * @param valueDtype Type of the table values.
   * @param options carries optional attributes values
   * @return a new instance of HashTable
   * @see {@link org.tensorflow.op.core.HashTable}
   */
  public  HashTable hashTable(Class keyDtype, Class valueDtype,
      HashTable.Options... options) {
    return HashTable.create(scope, keyDtype, valueDtype, options);
  }

  /**
   * Adds an {@link AsString} operation to the graph
   *
   * @param input 
   * @param options carries optional attributes values
   * @return a new instance of AsString
   * @see {@link org.tensorflow.op.core.AsString}
   */
  public  AsString asString(Operand input, AsString.Options... options) {
    return AsString.create(scope, input, options);
  }

  /**
   * Adds an {@link RFFT2D} operation to the graph
   *
   * @param input A float32 tensor.
   * @param fftLength An int32 tensor of shape [2]. The FFT length for each dimension.
   * @return a new instance of RFFT2D
   * @see {@link org.tensorflow.op.core.RFFT2D}
   */
  public RFFT2D rFFT2D(Operand input, Operand fftLength) {
    return RFFT2D.create(scope, input, fftLength);
  }

  /**
   * Adds an {@link Restore} operation to the graph
   *
   * @param filePattern Must have a single element. The pattern of the files from
   * @param tensorName Must have a single element. The name of the tensor to be
   * @param dt The type of the tensor to be restored.
   * @param options carries optional attributes values
   * @return a new instance of Restore
   * @see {@link org.tensorflow.op.core.Restore}
   */
  public  Restore restore(Operand filePattern, Operand tensorName,
      Class dt, Restore.Options... options) {
    return Restore.create(scope, filePattern, tensorName, dt, options);
  }

  /**
   * Adds an {@link Reshape} operation to the graph
   *
   * @param tensor 
   * @param shape Defines the shape of the output tensor.
   * @return a new instance of Reshape
   * @see {@link org.tensorflow.op.core.Reshape}
   */
  public  Reshape reshape(Operand tensor, Operand shape) {
    return Reshape.create(scope, tensor, shape);
  }

  /**
   * Adds an {@link WriteFile} operation to the graph
   *
   * @param filename scalar. The name of the file to which we write the contents.
   * @param contents scalar. The content to be written to the output file.
   * @return a new instance of WriteFile
   * @see {@link org.tensorflow.op.core.WriteFile}
   */
  public WriteFile writeFile(Operand filename, Operand contents) {
    return WriteFile.create(scope, filename, contents);
  }

  /**
   * Adds an {@link Cumprod} operation to the graph
   *
   * @param x A `Tensor`. Must be one of the following types: `float32`, `float64`,
   * @param axis A `Tensor` of type `int32` (default: 0). Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of Cumprod
   * @see {@link org.tensorflow.op.core.Cumprod}
   */
  public  Cumprod cumprod(Operand x, Operand axis,
      Cumprod.Options... options) {
    return Cumprod.create(scope, x, axis, options);
  }

  /**
   * Adds an {@link StageClear} operation to the graph
   *
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of StageClear
   * @see {@link org.tensorflow.op.core.StageClear}
   */
  public StageClear stageClear(List> dtypes, StageClear.Options... options) {
    return StageClear.create(scope, dtypes, options);
  }

  /**
   * Adds an {@link IFFT} operation to the graph
   *
   * @param input A complex64 tensor.
   * @return a new instance of IFFT
   * @see {@link org.tensorflow.op.core.IFFT}
   */
  public  IFFT iFFT(Operand input) {
    return IFFT.create(scope, input);
  }

  /**
   * Adds an {@link BigQueryReader} operation to the graph
   *
   * @param projectId GCP project ID.
   * @param datasetId BigQuery Dataset ID.
   * @param tableId Table to read.
   * @param columns List of columns to read. Leave empty to read all columns.
   * @param timestampMillis Table snapshot timestamp in millis since epoch. Relative
   * @param options carries optional attributes values
   * @return a new instance of BigQueryReader
   * @see {@link org.tensorflow.op.core.BigQueryReader}
   */
  public BigQueryReader bigQueryReader(String projectId, String datasetId, String tableId,
      List columns, Long timestampMillis, BigQueryReader.Options... options) {
    return BigQueryReader.create(scope, projectId, datasetId, tableId, columns, timestampMillis, options);
  }

  /**
   * Adds an {@link ScatterDiv} operation to the graph
   *
   * @param ref Should be from a `Variable` node.
   * @param indices A tensor of indices into the first dimension of `ref`.
   * @param updates A tensor of values that `ref` is divided by.
   * @param options carries optional attributes values
   * @return a new instance of ScatterDiv
   * @see {@link org.tensorflow.op.core.ScatterDiv}
   */
  public  ScatterDiv scatterDiv(Operand ref, Operand indices,
      Operand updates, ScatterDiv.Options... options) {
    return ScatterDiv.create(scope, ref, indices, updates, options);
  }

  /**
   * Adds an {@link CropAndResize} operation to the graph
   *
   * @param image A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
   * @param boxes A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor
   * @param boxInd A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
   * @param cropSize A 1-D tensor of 2 elements, `size = [crop_height, crop_width]`. All
   * @param options carries optional attributes values
   * @return a new instance of CropAndResize
   * @see {@link org.tensorflow.op.core.CropAndResize}
   */
  public  CropAndResize cropAndResize(Operand image, Operand boxes,
      Operand boxInd, Operand cropSize, CropAndResize.Options... options) {
    return CropAndResize.create(scope, image, boxes, boxInd, cropSize, options);
  }

  /**
   * Adds an {@link BarrierClose} operation to the graph
   *
   * @param handle The handle to a barrier.
   * @param options carries optional attributes values
   * @return a new instance of BarrierClose
   * @see {@link org.tensorflow.op.core.BarrierClose}
   */
  public BarrierClose barrierClose(Operand handle, BarrierClose.Options... options) {
    return BarrierClose.create(scope, handle, options);
  }

  /**
   * Adds an {@link Tanh} operation to the graph
   *
   * @param x 
   * @return a new instance of Tanh
   * @see {@link org.tensorflow.op.core.Tanh}
   */
  public  Tanh tanh(Operand x) {
    return Tanh.create(scope, x);
  }

  /**
   * Adds an {@link MaxPoolV2} operation to the graph
   *
   * @param input 4-D input to pool over.
   * @param ksize The size of the window for each dimension of the input tensor.
   * @param strides The stride of the sliding window for each dimension of the
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of MaxPoolV2
   * @see {@link org.tensorflow.op.core.MaxPoolV2}
   */
  public  MaxPoolV2 maxPoolV2(Operand input, Operand ksize,
      Operand strides, String padding, MaxPoolV2.Options... options) {
    return MaxPoolV2.create(scope, input, ksize, strides, padding, options);
  }

  /**
   * Adds an {@link BatchNormWithGlobalNormalizationGrad} operation to the graph
   *
   * @param t A 4D input Tensor.
   * @param m A 1D mean Tensor with size matching the last dimension of t.
   * @param v A 1D variance Tensor with size matching the last dimension of t.
   * @param gamma A 1D gamma Tensor with size matching the last dimension of t.
   * @param backprop 4D backprop Tensor.
   * @param varianceEpsilon A small float number to avoid dividing by 0.
   * @param scaleAfterNormalization A bool indicating whether the resulted tensor
   * @return a new instance of BatchNormWithGlobalNormalizationGrad
   * @see {@link org.tensorflow.op.core.BatchNormWithGlobalNormalizationGrad}
   */
  public  BatchNormWithGlobalNormalizationGrad batchNormWithGlobalNormalizationGrad(Operand t,
      Operand m, Operand v, Operand gamma, Operand backprop, Float varianceEpsilon,
      Boolean scaleAfterNormalization) {
    return BatchNormWithGlobalNormalizationGrad.create(scope, t, m, v, gamma, backprop, varianceEpsilon, scaleAfterNormalization);
  }

  /**
   * Adds an {@link ShuffleDataset} operation to the graph
   *
   * @param inputDataset 
   * @param bufferSize The number of output elements to buffer in an iterator over
   * @param seed A scalar seed for the random number generator. If either `seed` or
   * @param seed2 A second scalar seed to avoid seed collision.
   * @param outputTypes 
   * @param outputShapes 
   * @param options carries optional attributes values
   * @return a new instance of ShuffleDataset
   * @see {@link org.tensorflow.op.core.ShuffleDataset}
   */
  public ShuffleDataset shuffleDataset(Operand inputDataset, Operand bufferSize,
      Operand seed, Operand seed2, List> outputTypes, List outputShapes,
      ShuffleDataset.Options... options) {
    return ShuffleDataset.create(scope, inputDataset, bufferSize, seed, seed2, outputTypes, outputShapes, options);
  }

  /**
   * Adds an {@link DebugGradientIdentity} operation to the graph
   *
   * @param input 
   * @return a new instance of DebugGradientIdentity
   * @see {@link org.tensorflow.op.core.DebugGradientIdentity}
   */
  public  DebugGradientIdentity debugGradientIdentity(Operand input) {
    return DebugGradientIdentity.create(scope, input);
  }

  /**
   * Adds an {@link ClipByValue} operation to the graph
   *
   * @param t A `Tensor`.
   * @param clipValueMin A 0-D (scalar) `Tensor`, or a `Tensor` with the same shape
   * @param clipValueMax A 0-D (scalar) `Tensor`, or a `Tensor` with the same shape
   * @return a new instance of ClipByValue
   * @see {@link org.tensorflow.op.core.ClipByValue}
   */
  public  ClipByValue clipByValue(Operand t, Operand clipValueMin,
      Operand clipValueMax) {
    return ClipByValue.create(scope, t, clipValueMin, clipValueMax);
  }

  /**
   * Adds an {@link BitwiseAnd} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of BitwiseAnd
   * @see {@link org.tensorflow.op.core.BitwiseAnd}
   */
  public  BitwiseAnd bitwiseAnd(Operand x, Operand y) {
    return BitwiseAnd.create(scope, x, y);
  }

  /**
   * Adds an {@link TextLineDataset} operation to the graph
   *
   * @param filenames A scalar or a vector containing the name(s) of the file(s) to be
   * @param compressionType A scalar containing either (i) the empty string (no
   * @param bufferSize A scalar containing the number of bytes to buffer.
   * @return a new instance of TextLineDataset
   * @see {@link org.tensorflow.op.core.TextLineDataset}
   */
  public TextLineDataset textLineDataset(Operand filenames, Operand compressionType,
      Operand bufferSize) {
    return TextLineDataset.create(scope, filenames, compressionType, bufferSize);
  }

  /**
   * Adds an {@link RefSwitch} operation to the graph
   *
   * @param data The ref tensor to be forwarded to the appropriate output.
   * @param pred A scalar that specifies which output port will receive data.
   * @return a new instance of RefSwitch
   * @see {@link org.tensorflow.op.core.RefSwitch}
   */
  public  RefSwitch refSwitch(Operand data, Operand pred) {
    return RefSwitch.create(scope, data, pred);
  }

  /**
   * Adds an {@link ScatterNdUpdate} operation to the graph
   *
   * @param ref A mutable Tensor. Should be from a Variable node.
   * @param indices A Tensor. Must be one of the following types: int32, int64.
   * @param updates A Tensor. Must have the same type as ref. A tensor of updated
   * @param options carries optional attributes values
   * @return a new instance of ScatterNdUpdate
   * @see {@link org.tensorflow.op.core.ScatterNdUpdate}
   */
  public  ScatterNdUpdate scatterNdUpdate(Operand ref,
      Operand indices, Operand updates, ScatterNdUpdate.Options... options) {
    return ScatterNdUpdate.create(scope, ref, indices, updates, options);
  }

  /**
   * Adds an {@link LinSpace} operation to the graph
   *
   * @param start 0-D tensor. First entry in the range.
   * @param stop 0-D tensor. Last entry in the range.
   * @param num 0-D tensor. Number of values to generate.
   * @return a new instance of LinSpace
   * @see {@link org.tensorflow.op.core.LinSpace}
   */
  public  LinSpace linSpace(Operand start,
      Operand stop, Operand num) {
    return LinSpace.create(scope, start, stop, num);
  }

  /**
   * Adds an {@link RefSelect} operation to the graph
   *
   * @param index A scalar that determines the input that gets selected.
   * @param inputs A list of ref tensors, one of which will be forwarded to `output`.
   * @return a new instance of RefSelect
   * @see {@link org.tensorflow.op.core.RefSelect}
   */
  public  RefSelect refSelect(Operand index, Operand inputs) {
    return RefSelect.create(scope, index, inputs);
  }

  /**
   * Adds an {@link Rpc} operation to the graph
   *
   * @param address `0-D` or `1-D`.  The address (i.e. host_name:port) of the RPC server.
   * @param method `0-D` or `1-D`.  The method address on the RPC server.
   * @param request `0-D` or `1-D`.  Serialized proto strings: the rpc request argument.
   * @param options carries optional attributes values
   * @return a new instance of Rpc
   * @see {@link org.tensorflow.op.core.Rpc}
   */
  public Rpc rpc(Operand address, Operand method, Operand request,
      Rpc.Options... options) {
    return Rpc.create(scope, address, method, request, options);
  }

  /**
   * Adds an {@link ConjugateTranspose} operation to the graph
   *
   * @param x 
   * @param perm 
   * @return a new instance of ConjugateTranspose
   * @see {@link org.tensorflow.op.core.ConjugateTranspose}
   */
  public  ConjugateTranspose conjugateTranspose(Operand x,
      Operand perm) {
    return ConjugateTranspose.create(scope, x, perm);
  }

  /**
   * Adds an {@link TensorArrayGrad} operation to the graph
   *
   * @param handle The handle to the forward TensorArray.
   * @param flowIn A float scalar that enforces proper chaining of operations.
   * @param source The gradient source string, used to decide which gradient TensorArray
   * @return a new instance of TensorArrayGrad
   * @see {@link org.tensorflow.op.core.TensorArrayGrad}
   */
  public TensorArrayGrad tensorArrayGrad(Operand handle, Operand flowIn, String source) {
    return TensorArrayGrad.create(scope, handle, flowIn, source);
  }

  /**
   * Adds an {@link Round} operation to the graph
   *
   * @param x 
   * @return a new instance of Round
   * @see {@link org.tensorflow.op.core.Round}
   */
  public  Round round(Operand x) {
    return Round.create(scope, x);
  }

  /**
   * Adds an {@link Dequantize} operation to the graph
   *
   * @param input 
   * @param minRange The minimum scalar value possibly produced for the input.
   * @param maxRange The maximum scalar value possibly produced for the input.
   * @param options carries optional attributes values
   * @return a new instance of Dequantize
   * @see {@link org.tensorflow.op.core.Dequantize}
   */
  public  Dequantize dequantize(Operand input, Operand minRange,
      Operand maxRange, Dequantize.Options... options) {
    return Dequantize.create(scope, input, minRange, maxRange, options);
  }

  /**
   * Adds an {@link DeserializeSparse} operation to the graph
   *
   * @param serializedSparse The serialized `SparseTensor` objects. The last dimension
   * @param dtype The `dtype` of the serialized `SparseTensor` objects.
   * @return a new instance of DeserializeSparse
   * @see {@link org.tensorflow.op.core.DeserializeSparse}
   */
  public  DeserializeSparse deserializeSparse(Operand serializedSparse,
      Class dtype) {
    return DeserializeSparse.create(scope, serializedSparse, dtype);
  }

  /**
   * Adds an {@link NotEqual} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of NotEqual
   * @see {@link org.tensorflow.op.core.NotEqual}
   */
  public  NotEqual notEqual(Operand x, Operand y) {
    return NotEqual.create(scope, x, y);
  }

  /**
   * Adds an {@link PlaceholderV2} operation to the graph
   *
   * @param dtype The type of elements in the tensor.
   * @param shape The shape of the tensor. The shape can be any partially-specified
   * @return a new instance of PlaceholderV2
   * @see {@link org.tensorflow.op.core.PlaceholderV2}
   */
  public  PlaceholderV2 placeholderV2(Class dtype, Shape shape) {
    return PlaceholderV2.create(scope, dtype, shape);
  }

  /**
   * Adds an {@link ArgMax} operation to the graph
   *
   * @param input 
   * @param dimension int32 or int64, must be in the range `[-rank(input), rank(input))`.
   * @param outputType 
   * @return a new instance of ArgMax
   * @see {@link org.tensorflow.op.core.ArgMax}
   */
  public  ArgMax argMax(Operand input,
      Operand dimension, Class outputType) {
    return ArgMax.create(scope, input, dimension, outputType);
  }

  /**
   * Adds an {@link Snapshot} operation to the graph
   *
   * @param input 
   * @return a new instance of Snapshot
   * @see {@link org.tensorflow.op.core.Snapshot}
   */
  public  Snapshot snapshot(Operand input) {
    return Snapshot.create(scope, input);
  }

  /**
   * Adds an {@link NonMaxSuppression} operation to the graph
   *
   * @param boxes A 2-D float tensor of shape `[num_boxes, 4]`.
   * @param scores A 1-D float tensor of shape `[num_boxes]` representing a single
   * @param maxOutputSize A scalar integer tensor representing the maximum number of
   * @param options carries optional attributes values
   * @return a new instance of NonMaxSuppression
   * @see {@link org.tensorflow.op.core.NonMaxSuppression}
   */
  public NonMaxSuppression nonMaxSuppression(Operand boxes, Operand scores,
      Operand maxOutputSize, NonMaxSuppression.Options... options) {
    return NonMaxSuppression.create(scope, boxes, scores, maxOutputSize, options);
  }

  /**
   * Adds an {@link ControlTrigger} operation to the graph
   *
   * @return a new instance of ControlTrigger
   * @see {@link org.tensorflow.op.core.ControlTrigger}
   */
  public ControlTrigger controlTrigger() {
    return ControlTrigger.create(scope);
  }

  /**
   * Adds an {@link Print} operation to the graph
   *
   * @param input The tensor passed to `output`
   * @param data A list of tensors to print out when op is evaluated.
   * @param options carries optional attributes values
   * @return a new instance of Print
   * @see {@link org.tensorflow.op.core.Print}
   */
  public  Print print(Operand input, Iterable> data, Print.Options... options) {
    return Print.create(scope, input, data, options);
  }

  /**
   * Adds an {@link DenseToSparseBatchDataset} operation to the graph
   *
   * @param inputDataset A handle to an input dataset. Must have a single component.
   * @param batchSize A scalar representing the number of elements to accumulate in a
   * @param rowShape A vector representing the dense shape of each row in the produced
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of DenseToSparseBatchDataset
   * @see {@link org.tensorflow.op.core.DenseToSparseBatchDataset}
   */
  public DenseToSparseBatchDataset denseToSparseBatchDataset(Operand inputDataset,
      Operand batchSize, Operand rowShape, List> outputTypes,
      List outputShapes) {
    return DenseToSparseBatchDataset.create(scope, inputDataset, batchSize, rowShape, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link LogicalOr} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of LogicalOr
   * @see {@link org.tensorflow.op.core.LogicalOr}
   */
  public LogicalOr logicalOr(Operand x, Operand y) {
    return LogicalOr.create(scope, x, y);
  }

  /**
   * Adds an {@link RangeDataset} operation to the graph
   *
   * @param start corresponds to start in python's xrange().
   * @param stop corresponds to stop in python's xrange().
   * @param step corresponds to step in python's xrange().
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of RangeDataset
   * @see {@link org.tensorflow.op.core.RangeDataset}
   */
  public RangeDataset rangeDataset(Operand start, Operand stop, Operand step,
      List> outputTypes, List outputShapes) {
    return RangeDataset.create(scope, start, stop, step, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link AddManySparseToTensorsMap} operation to the graph
   *
   * @param sparseIndices 2-D.  The `indices` of the minibatch `SparseTensor`.
   * @param sparseValues 1-D.  The `values` of the minibatch `SparseTensor`.
   * @param sparseShape 1-D.  The `shape` of the minibatch `SparseTensor`.
   * @param options carries optional attributes values
   * @return a new instance of AddManySparseToTensorsMap
   * @see {@link org.tensorflow.op.core.AddManySparseToTensorsMap}
   */
  public  AddManySparseToTensorsMap addManySparseToTensorsMap(Operand sparseIndices,
      Operand sparseValues, Operand sparseShape,
      AddManySparseToTensorsMap.Options... options) {
    return AddManySparseToTensorsMap.create(scope, sparseIndices, sparseValues, sparseShape, options);
  }

  /**
   * Adds an {@link StopGradient} operation to the graph
   *
   * @param input 
   * @return a new instance of StopGradient
   * @see {@link org.tensorflow.op.core.StopGradient}
   */
  public  StopGradient stopGradient(Operand input) {
    return StopGradient.create(scope, input);
  }

  /**
   * Adds an {@link ResizeArea} operation to the graph
   *
   * @param images 4-D with shape `[batch, height, width, channels]`.
   * @param size = A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
   * @param options carries optional attributes values
   * @return a new instance of ResizeArea
   * @see {@link org.tensorflow.op.core.ResizeArea}
   */
  public  ResizeArea resizeArea(Operand images, Operand size,
      ResizeArea.Options... options) {
    return ResizeArea.create(scope, images, size, options);
  }

  /**
   * Adds an {@link DenseToSparseSetOperation} operation to the graph
   *
   * @param set1 `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`.
   * @param set2Indices 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
   * @param set2Values 1D `Tensor`, values of a `SparseTensor`. Must be in row-major
   * @param set2Shape 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must
   * @param setOperation 
   * @param options carries optional attributes values
   * @return a new instance of DenseToSparseSetOperation
   * @see {@link org.tensorflow.op.core.DenseToSparseSetOperation}
   */
  public  DenseToSparseSetOperation denseToSparseSetOperation(Operand set1,
      Operand set2Indices, Operand set2Values, Operand set2Shape,
      String setOperation, DenseToSparseSetOperation.Options... options) {
    return DenseToSparseSetOperation.create(scope, set1, set2Indices, set2Values, set2Shape, setOperation, options);
  }

  /**
   * Adds an {@link SegmentMin} operation to the graph
   *
   * @param data 
   * @param segmentIds A 1-D tensor whose rank is equal to the rank of `data`'s
   * @return a new instance of SegmentMin
   * @see {@link org.tensorflow.op.core.SegmentMin}
   */
  public  SegmentMin segmentMin(Operand data,
      Operand segmentIds) {
    return SegmentMin.create(scope, data, segmentIds);
  }

  /**
   * Adds an {@link Sigmoid} operation to the graph
   *
   * @param x 
   * @return a new instance of Sigmoid
   * @see {@link org.tensorflow.op.core.Sigmoid}
   */
  public  Sigmoid sigmoid(Operand x) {
    return Sigmoid.create(scope, x);
  }

  /**
   * Adds an {@link Imag} operation to the graph
   *
   * @param input 
   * @param Tout 
   * @return a new instance of Imag
   * @see {@link org.tensorflow.op.core.Imag}
   */
  public  Imag imag(Operand input, Class Tout) {
    return Imag.create(scope, input, Tout);
  }

  /**
   * Adds an {@link SparseReduceSum} operation to the graph
   *
   * @param inputIndices 2-D.  `N x R` matrix with the indices of non-empty values in a
   * @param inputValues 1-D.  `N` non-empty values corresponding to `input_indices`.
   * @param inputShape 1-D.  Shape of the input SparseTensor.
   * @param reductionAxes 1-D.  Length-`K` vector containing the reduction axes.
   * @param options carries optional attributes values
   * @return a new instance of SparseReduceSum
   * @see {@link org.tensorflow.op.core.SparseReduceSum}
   */
  public  SparseReduceSum sparseReduceSum(Operand inputIndices, Operand inputValues,
      Operand inputShape, Operand reductionAxes,
      SparseReduceSum.Options... options) {
    return SparseReduceSum.create(scope, inputIndices, inputValues, inputShape, reductionAxes, options);
  }

  /**
   * Adds an {@link ResourceApplyAdagrad} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ResourceApplyAdagrad
   * @see {@link org.tensorflow.op.core.ResourceApplyAdagrad}
   */
  public  ResourceApplyAdagrad resourceApplyAdagrad(Operand var, Operand accum,
      Operand lr, Operand grad, ResourceApplyAdagrad.Options... options) {
    return ResourceApplyAdagrad.create(scope, var, accum, lr, grad, options);
  }

  /**
   * Adds an {@link Dilation2D} operation to the graph
   *
   * @param input 4-D with shape `[batch, in_height, in_width, depth]`.
   * @param filter 3-D with shape `[filter_height, filter_width, depth]`.
   * @param strides The stride of the sliding window for each dimension of the input
   * @param rates The input stride for atrous morphological dilation. Must be:
   * @param padding The type of padding algorithm to use.
   * @return a new instance of Dilation2D
   * @see {@link org.tensorflow.op.core.Dilation2D}
   */
  public  Dilation2D dilation2D(Operand input, Operand filter,
      List strides, List rates, String padding) {
    return Dilation2D.create(scope, input, filter, strides, rates, padding);
  }

  /**
   * Adds an {@link TakeDataset} operation to the graph
   *
   * @param inputDataset 
   * @param count A scalar representing the number of elements from the `input_dataset`
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of TakeDataset
   * @see {@link org.tensorflow.op.core.TakeDataset}
   */
  public TakeDataset takeDataset(Operand inputDataset, Operand count,
      List> outputTypes, List outputShapes) {
    return TakeDataset.create(scope, inputDataset, count, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link Assign} operation to the graph
   *
   * @param ref Should be from a `Variable` node. May be uninitialized.
   * @param value The value to be assigned to the variable.
   * @param options carries optional attributes values
   * @return a new instance of Assign
   * @see {@link org.tensorflow.op.core.Assign}
   */
  public  Assign assign(Operand ref, Operand value, Assign.Options... options) {
    return Assign.create(scope, ref, value, options);
  }

  /**
   * Adds an {@link AvgPool3DGrad} operation to the graph
   *
   * @param origInputShape The original input dimensions.
   * @param grad Output backprop of shape `[batch, depth, rows, cols, channels]`.
   * @param ksize 1-D tensor of length 5. The size of the window for each dimension of
   * @param strides 1-D tensor of length 5. The stride of the sliding window for each
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of AvgPool3DGrad
   * @see {@link org.tensorflow.op.core.AvgPool3DGrad}
   */
  public  AvgPool3DGrad avgPool3DGrad(Operand origInputShape,
      Operand grad, List ksize, List strides, String padding,
      AvgPool3DGrad.Options... options) {
    return AvgPool3DGrad.create(scope, origInputShape, grad, ksize, strides, padding, options);
  }

  /**
   * Adds an {@link SparseDenseCwiseDiv} operation to the graph
   *
   * @param spIndices 2-D.  `N x R` matrix with the indices of non-empty values in a
   * @param spValues 1-D.  `N` non-empty values corresponding to `sp_indices`.
   * @param spShape 1-D.  Shape of the input SparseTensor.
   * @param dense `R`-D.  The dense Tensor operand.
   * @return a new instance of SparseDenseCwiseDiv
   * @see {@link org.tensorflow.op.core.SparseDenseCwiseDiv}
   */
  public  SparseDenseCwiseDiv sparseDenseCwiseDiv(Operand spIndices,
      Operand spValues, Operand spShape, Operand dense) {
    return SparseDenseCwiseDiv.create(scope, spIndices, spValues, spShape, dense);
  }

  /**
   * Adds an {@link Squeeze} operation to the graph
   *
   * @param input The `input` to squeeze.
   * @param options carries optional attributes values
   * @return a new instance of Squeeze
   * @see {@link org.tensorflow.op.core.Squeeze}
   */
  public  Squeeze squeeze(Operand input, Squeeze.Options... options) {
    return Squeeze.create(scope, input, options);
  }

  /**
   * Adds an {@link RestoreSlice} operation to the graph
   *
   * @param filePattern Must have a single element. The pattern of the files from
   * @param tensorName Must have a single element. The name of the tensor to be
   * @param shapeAndSlice Scalar. The shapes and slice specifications to use when
   * @param dt The type of the tensor to be restored.
   * @param options carries optional attributes values
   * @return a new instance of RestoreSlice
   * @see {@link org.tensorflow.op.core.RestoreSlice}
   */
  public  RestoreSlice restoreSlice(Operand filePattern, Operand tensorName,
      Operand shapeAndSlice, Class dt, RestoreSlice.Options... options) {
    return RestoreSlice.create(scope, filePattern, tensorName, shapeAndSlice, dt, options);
  }

  /**
   * Adds an {@link QueueDequeueMany} operation to the graph
   *
   * @param handle The handle to a queue.
   * @param n The number of tuples to dequeue.
   * @param componentTypes The type of each component in a tuple.
   * @param options carries optional attributes values
   * @return a new instance of QueueDequeueMany
   * @see {@link org.tensorflow.op.core.QueueDequeueMany}
   */
  public QueueDequeueMany queueDequeueMany(Operand handle, Operand n,
      List> componentTypes, QueueDequeueMany.Options... options) {
    return QueueDequeueMany.create(scope, handle, n, componentTypes, options);
  }

  /**
   * Adds an {@link StringToNumber} operation to the graph
   *
   * @param stringTensor 
   * @param outType The numeric type to interpret each string in `string_tensor` as.
   * @return a new instance of StringToNumber
   * @see {@link org.tensorflow.op.core.StringToNumber}
   */
  public  StringToNumber stringToNumber(Operand stringTensor,
      Class outType) {
    return StringToNumber.create(scope, stringTensor, outType);
  }

  /**
   * Adds an {@link MapIncompleteSize} operation to the graph
   *
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of MapIncompleteSize
   * @see {@link org.tensorflow.op.core.MapIncompleteSize}
   */
  public MapIncompleteSize mapIncompleteSize(List> dtypes,
      MapIncompleteSize.Options... options) {
    return MapIncompleteSize.create(scope, dtypes, options);
  }

  /**
   * Adds an {@link StringToHashBucket} operation to the graph
   *
   * @param stringTensor 
   * @param numBuckets The number of buckets.
   * @return a new instance of StringToHashBucket
   * @see {@link org.tensorflow.op.core.StringToHashBucket}
   */
  public StringToHashBucket stringToHashBucket(Operand stringTensor, Long numBuckets) {
    return StringToHashBucket.create(scope, stringTensor, numBuckets);
  }

  /**
   * Adds an {@link GatherV2} operation to the graph
   *
   * @param params The tensor from which to gather values. Must be at least rank
   * @param indices Index tensor. Must be in range `[0, params.shape[axis])`.
   * @param axis The axis in `params` to gather `indices` from. Defaults to the first
   * @return a new instance of GatherV2
   * @see {@link org.tensorflow.op.core.GatherV2}
   */
  public  GatherV2 gatherV2(Operand params,
      Operand indices, Operand axis) {
    return GatherV2.create(scope, params, indices, axis);
  }

  /**
   * Adds an {@link SoftmaxCrossEntropyWithLogits} operation to the graph
   *
   * @param features batch_size x num_classes matrix
   * @param labels batch_size x num_classes matrix
   * @return a new instance of SoftmaxCrossEntropyWithLogits
   * @see {@link org.tensorflow.op.core.SoftmaxCrossEntropyWithLogits}
   */
  public  SoftmaxCrossEntropyWithLogits softmaxCrossEntropyWithLogits(Operand features,
      Operand labels) {
    return SoftmaxCrossEntropyWithLogits.create(scope, features, labels);
  }

  /**
   * Adds an {@link SparseSegmentSqrtN} operation to the graph
   *
   * @param data 
   * @param indices A 1-D tensor. Has same rank as `segment_ids`.
   * @param segmentIds A 1-D tensor. Values should be sorted and can be repeated.
   * @return a new instance of SparseSegmentSqrtN
   * @see {@link org.tensorflow.op.core.SparseSegmentSqrtN}
   */
  public  SparseSegmentSqrtN sparseSegmentSqrtN(Operand data,
      Operand indices, Operand segmentIds) {
    return SparseSegmentSqrtN.create(scope, data, indices, segmentIds);
  }

  /**
   * Adds an {@link ApplyAdagrad} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ApplyAdagrad
   * @see {@link org.tensorflow.op.core.ApplyAdagrad}
   */
  public  ApplyAdagrad applyAdagrad(Operand var, Operand accum, Operand lr,
      Operand grad, ApplyAdagrad.Options... options) {
    return ApplyAdagrad.create(scope, var, accum, lr, grad, options);
  }

  /**
   * Adds an {@link TensorArrayWrite} operation to the graph
   *
   * @param handle The handle to a TensorArray.
   * @param index The position to write to inside the TensorArray.
   * @param value The tensor to write to the TensorArray.
   * @param flowIn A float scalar that enforces proper chaining of operations.
   * @return a new instance of TensorArrayWrite
   * @see {@link org.tensorflow.op.core.TensorArrayWrite}
   */
  public  TensorArrayWrite tensorArrayWrite(Operand handle, Operand index,
      Operand value, Operand flowIn) {
    return TensorArrayWrite.create(scope, handle, index, value, flowIn);
  }

  /**
   * Adds an {@link SegmentSum} operation to the graph
   *
   * @param data 
   * @param segmentIds A 1-D tensor whose rank is equal to the rank of `data`'s
   * @return a new instance of SegmentSum
   * @see {@link org.tensorflow.op.core.SegmentSum}
   */
  public  SegmentSum segmentSum(Operand data, Operand segmentIds) {
    return SegmentSum.create(scope, data, segmentIds);
  }

  /**
   * Adds an {@link Igamma} operation to the graph
   *
   * @param a 
   * @param x 
   * @return a new instance of Igamma
   * @see {@link org.tensorflow.op.core.Igamma}
   */
  public  Igamma igamma(Operand a, Operand x) {
    return Igamma.create(scope, a, x);
  }

  /**
   * Adds an {@link Inv} operation to the graph
   *
   * @param x 
   * @return a new instance of Inv
   * @see {@link org.tensorflow.op.core.Inv}
   */
  public  Inv inv(Operand x) {
    return Inv.create(scope, x);
  }

  /**
   * Adds an {@link TensorArrayClose} operation to the graph
   *
   * @param handle The handle to a TensorArray (output of TensorArray or TensorArrayGrad).
   * @return a new instance of TensorArrayClose
   * @see {@link org.tensorflow.op.core.TensorArrayClose}
   */
  public TensorArrayClose tensorArrayClose(Operand handle) {
    return TensorArrayClose.create(scope, handle);
  }

  /**
   * Adds an {@link Where3} operation to the graph
   *
   * @param condition 
   * @param x = A `Tensor` which may have the same shape as `condition`.
   * @param y = A `Tensor` with the same type and shape as `x`.
   * @return a new instance of Where3
   * @see {@link org.tensorflow.op.core.Where3}
   */
  public  Where3 where3(Operand condition, Operand x, Operand y) {
    return Where3.create(scope, condition, x, y);
  }

  /**
   * Adds an {@link LogicalAnd} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of LogicalAnd
   * @see {@link org.tensorflow.op.core.LogicalAnd}
   */
  public LogicalAnd logicalAnd(Operand x, Operand y) {
    return LogicalAnd.create(scope, x, y);
  }

  /**
   * Adds an {@link Timestamp} operation to the graph
   *
   * @return a new instance of Timestamp
   * @see {@link org.tensorflow.op.core.Timestamp}
   */
  public Timestamp timestamp() {
    return Timestamp.create(scope);
  }

  /**
   * Adds an {@link AdjustContrast} operation to the graph
   *
   * @param images Images to adjust.  At least 3-D.
   * @param contrastFactor A float multiplier for adjusting contrast.
   * @return a new instance of AdjustContrast
   * @see {@link org.tensorflow.op.core.AdjustContrast}
   */
  public AdjustContrast adjustContrast(Operand images, Operand contrastFactor) {
    return AdjustContrast.create(scope, images, contrastFactor);
  }

  /**
   * Adds an {@link MirrorPad} operation to the graph
   *
   * @param input The input tensor to be padded.
   * @param paddings A two-column matrix specifying the padding sizes. The number of
   * @param mode Either `REFLECT` or `SYMMETRIC`. In reflect mode the padded regions
   * @return a new instance of MirrorPad
   * @see {@link org.tensorflow.op.core.MirrorPad}
   */
  public  MirrorPad mirrorPad(Operand input, Operand paddings,
      String mode) {
    return MirrorPad.create(scope, input, paddings, mode);
  }

  /**
   * Adds an {@link TensorSliceDataset} operation to the graph
   *
   * @param components 
   * @param outputShapes 
   * @return a new instance of TensorSliceDataset
   * @see {@link org.tensorflow.op.core.TensorSliceDataset}
   */
  public TensorSliceDataset tensorSliceDataset(Iterable> components,
      List outputShapes) {
    return TensorSliceDataset.create(scope, components, outputShapes);
  }

  /**
   * Adds an {@link LoadAndRemapMatrix} operation to the graph
   *
   * @param ckptPath Path to the TensorFlow checkpoint (version 2, `TensorBundle`) from
   * @param oldTensorName Name of the 2-D `Tensor` to load from checkpoint.
   * @param rowRemapping An int `Tensor` of row remappings (generally created by
   * @param colRemapping An int `Tensor` of column remappings (generally created by
   * @param initializingValues A float `Tensor` containing  values to fill in for cells
   * @param numRows Number of rows (length of the 1st dimension) in the output matrix.
   * @param numCols Number of columns (length of the 2nd dimension) in the output matrix.
   * @param options carries optional attributes values
   * @return a new instance of LoadAndRemapMatrix
   * @see {@link org.tensorflow.op.core.LoadAndRemapMatrix}
   */
  public LoadAndRemapMatrix loadAndRemapMatrix(Operand ckptPath,
      Operand oldTensorName, Operand rowRemapping, Operand colRemapping,
      Operand initializingValues, Long numRows, Long numCols,
      LoadAndRemapMatrix.Options... options) {
    return LoadAndRemapMatrix.create(scope, ckptPath, oldTensorName, rowRemapping, colRemapping, initializingValues, numRows, numCols, options);
  }

  /**
   * Adds an {@link EnqueueInQueueDataset} operation to the graph
   *
   * @param queue 
   * @param components 
   * @return a new instance of EnqueueInQueueDataset
   * @see {@link org.tensorflow.op.core.EnqueueInQueueDataset}
   */
  public EnqueueInQueueDataset enqueueInQueueDataset(Operand queue,
      Iterable> components) {
    return EnqueueInQueueDataset.create(scope, queue, components);
  }

  /**
   * Adds an {@link Roll} operation to the graph
   *
   * @param input 
   * @param shift Dimension must be 0-D or 1-D. `shift[i]` specifies the number of places by which
   * @param axis Dimension must be 0-D or 1-D. `axis[i]` specifies the dimension that the shift
   * @return a new instance of Roll
   * @see {@link org.tensorflow.op.core.Roll}
   */
  public  Roll roll(Operand input, Operand shift,
      Operand axis) {
    return Roll.create(scope, input, shift, axis);
  }

  /**
   * Adds an {@link LeftShift} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of LeftShift
   * @see {@link org.tensorflow.op.core.LeftShift}
   */
  public  LeftShift leftShift(Operand x, Operand y) {
    return LeftShift.create(scope, x, y);
  }

  /**
   * Adds an {@link Abs} operation to the graph
   *
   * @param x 
   * @return a new instance of Abs
   * @see {@link org.tensorflow.op.core.Abs}
   */
  public  Abs abs(Operand x) {
    return Abs.create(scope, x);
  }

  /**
   * Adds an {@link GreaterEqual} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of GreaterEqual
   * @see {@link org.tensorflow.op.core.GreaterEqual}
   */
  public  GreaterEqual greaterEqual(Operand x, Operand y) {
    return GreaterEqual.create(scope, x, y);
  }

  /**
   * Adds an {@link HistogramFixedWidth} operation to the graph
   *
   * @param values Numeric `Tensor`.
   * @param valueRange Shape [2] `Tensor` of same `dtype` as `values`.
   * @param nbins Scalar `int32 Tensor`.  Number of histogram bins.
   * @param dtype 
   * @return a new instance of HistogramFixedWidth
   * @see {@link org.tensorflow.op.core.HistogramFixedWidth}
   */
  public  HistogramFixedWidth histogramFixedWidth(Operand values,
      Operand valueRange, Operand nbins, Class dtype) {
    return HistogramFixedWidth.create(scope, values, valueRange, nbins, dtype);
  }

  /**
   * Adds an {@link SplitV} operation to the graph
   *
   * @param value The tensor to split.
   * @param sizeSplits list containing the sizes of each output tensor along the split
   * @param axis 0-D.  The dimension along which to split.  Must be in the range
   * @param numSplit 
   * @return a new instance of SplitV
   * @see {@link org.tensorflow.op.core.SplitV}
   */
  public  SplitV splitV(Operand value, Operand sizeSplits,
      Operand axis, Long numSplit) {
    return SplitV.create(scope, value, sizeSplits, axis, numSplit);
  }

  /**
   * Adds an {@link RandomPoisson} operation to the graph
   *
   * @param shape 
   * @param rate 
   * @param options carries optional attributes values
   * @return a new instance of RandomPoisson
   * @see {@link org.tensorflow.op.core.RandomPoisson}
   */
  public  RandomPoisson randomPoisson(Operand shape,
      Operand rate, RandomPoisson.Options... options) {
    return RandomPoisson.create(scope, shape, rate, options);
  }

  /**
   * Adds an {@link AccumulatorNumAccumulated} operation to the graph
   *
   * @param handle The handle to an accumulator.
   * @return a new instance of AccumulatorNumAccumulated
   * @see {@link org.tensorflow.op.core.AccumulatorNumAccumulated}
   */
  public AccumulatorNumAccumulated accumulatorNumAccumulated(Operand handle) {
    return AccumulatorNumAccumulated.create(scope, handle);
  }

  /**
   * Adds an {@link BatchSelfAdjointEig} operation to the graph
   *
   * @param input 
   * @return a new instance of BatchSelfAdjointEig
   * @see {@link org.tensorflow.op.core.BatchSelfAdjointEig}
   */
  public  BatchSelfAdjointEig batchSelfAdjointEig(Operand input) {
    return BatchSelfAdjointEig.create(scope, input);
  }

  /**
   * Adds an {@link Complex} operation to the graph
   *
   * @param real 
   * @param imag 
   * @param Tout 
   * @return a new instance of Complex
   * @see {@link org.tensorflow.op.core.Complex}
   */
  public  Complex complex(Operand real, Operand imag, Class Tout) {
    return Complex.create(scope, real, imag, Tout);
  }

  /**
   * Adds an {@link FixedLengthRecordReader} operation to the graph
   *
   * @param recordBytes Number of bytes in the record.
   * @param options carries optional attributes values
   * @return a new instance of FixedLengthRecordReader
   * @see {@link org.tensorflow.op.core.FixedLengthRecordReader}
   */
  public FixedLengthRecordReader fixedLengthRecordReader(Long recordBytes,
      FixedLengthRecordReader.Options... options) {
    return FixedLengthRecordReader.create(scope, recordBytes, options);
  }

  /**
   * Adds an {@link Atan2} operation to the graph
   *
   * @param y 
   * @param x 
   * @return a new instance of Atan2
   * @see {@link org.tensorflow.op.core.Atan2}
   */
  public  Atan2 atan2(Operand y, Operand x) {
    return Atan2.create(scope, y, x);
  }

  /**
   * Adds an {@link LogMatrixDeterminant} operation to the graph
   *
   * @param input Shape is `[N, M, M]`.
   * @return a new instance of LogMatrixDeterminant
   * @see {@link org.tensorflow.op.core.LogMatrixDeterminant}
   */
  public  LogMatrixDeterminant logMatrixDeterminant(Operand input) {
    return LogMatrixDeterminant.create(scope, input);
  }

  /**
   * Adds an {@link ReaderReadUpTo} operation to the graph
   *
   * @param readerHandle Handle to a `Reader`.
   * @param queueHandle Handle to a `Queue`, with string work items.
   * @param numRecords number of records to read from `Reader`.
   * @return a new instance of ReaderReadUpTo
   * @see {@link org.tensorflow.op.core.ReaderReadUpTo}
   */
  public ReaderReadUpTo readerReadUpTo(Operand readerHandle, Operand queueHandle,
      Operand numRecords) {
    return ReaderReadUpTo.create(scope, readerHandle, queueHandle, numRecords);
  }

  /**
   * Adds an {@link Prod} operation to the graph
   *
   * @param input The tensor to reduce.
   * @param axis The dimensions to reduce. Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of Prod
   * @see {@link org.tensorflow.op.core.Prod}
   */
  public  Prod prod(Operand input, Operand axis,
      Prod.Options... options) {
    return Prod.create(scope, input, axis, options);
  }

  /**
   * Adds an {@link Diag} operation to the graph
   *
   * @param diagonal Rank k tensor where k is at most 1.
   * @return a new instance of Diag
   * @see {@link org.tensorflow.op.core.Diag}
   */
  public  Diag diag(Operand diagonal) {
    return Diag.create(scope, diagonal);
  }

  /**
   * Adds an {@link EditDistance} operation to the graph
   *
   * @param hypothesisIndices The indices of the hypothesis list SparseTensor.
   * @param hypothesisValues The values of the hypothesis list SparseTensor.
   * @param hypothesisShape The shape of the hypothesis list SparseTensor.
   * @param truthIndices The indices of the truth list SparseTensor.
   * @param truthValues The values of the truth list SparseTensor.
   * @param truthShape truth indices, vector.
   * @param options carries optional attributes values
   * @return a new instance of EditDistance
   * @see {@link org.tensorflow.op.core.EditDistance}
   */
  public  EditDistance editDistance(Operand hypothesisIndices, Operand hypothesisValues,
      Operand hypothesisShape, Operand truthIndices, Operand truthValues,
      Operand truthShape, EditDistance.Options... options) {
    return EditDistance.create(scope, hypothesisIndices, hypothesisValues, hypothesisShape, truthIndices, truthValues, truthShape, options);
  }

  /**
   * Adds an {@link NoOp} operation to the graph
   *
   * @return a new instance of NoOp
   * @see {@link org.tensorflow.op.core.NoOp}
   */
  public NoOp noOp() {
    return NoOp.create(scope);
  }

  /**
   * Adds an {@link Constant} operation to the graph
   *
   * @param shape the tensor shape.
   * @param data a buffer containing the tensor data.
   * @throws IllegalArgumentException If the tensor shape is not compatible with the buffer
   * @see {@link org.tensorflow.op.core.Constant}
   */
  public Constant constant(long[] shape, IntBuffer data) {
    return Constant.create(scope, shape, data);
  }

  /**
   * Adds an {@link ApplyRMSProp} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param ms Should be from a Variable().
   * @param mom Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param rho Decay rate. Must be a scalar.
   * @param momentum 
   * @param epsilon Ridge term. Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ApplyRMSProp
   * @see {@link org.tensorflow.op.core.ApplyRMSProp}
   */
  public  ApplyRMSProp applyRMSProp(Operand var, Operand ms, Operand mom,
      Operand lr, Operand rho, Operand momentum, Operand epsilon, Operand grad,
      ApplyRMSProp.Options... options) {
    return ApplyRMSProp.create(scope, var, ms, mom, lr, rho, momentum, epsilon, grad, options);
  }

  /**
   * Adds an {@link ResourceCountUpTo} operation to the graph
   *
   * @param resource Should be from a scalar `Variable` node.
   * @param limit If incrementing ref would bring it above limit, instead generates an
   * @param T 
   * @return a new instance of ResourceCountUpTo
   * @see {@link org.tensorflow.op.core.ResourceCountUpTo}
   */
  public  ResourceCountUpTo resourceCountUpTo(Operand resource, Long limit,
      Class T) {
    return ResourceCountUpTo.create(scope, resource, limit, T);
  }

  /**
   * Adds an {@link SparseConditionalAccumulator} operation to the graph
   *
   * @param dtype The type of the value being accumulated.
   * @param shape The shape of the values.
   * @param options carries optional attributes values
   * @return a new instance of SparseConditionalAccumulator
   * @see {@link org.tensorflow.op.core.SparseConditionalAccumulator}
   */
  public  SparseConditionalAccumulator sparseConditionalAccumulator(Class dtype, Shape shape,
      SparseConditionalAccumulator.Options... options) {
    return SparseConditionalAccumulator.create(scope, dtype, shape, options);
  }

  /**
   * Adds an {@link UnsortedSegmentMax} operation to the graph
   *
   * @param data 
   * @param segmentIds A 1-D tensor whose rank is equal to the rank of `data`'s
   * @param numSegments 
   * @return a new instance of UnsortedSegmentMax
   * @see {@link org.tensorflow.op.core.UnsortedSegmentMax}
   */
  public  UnsortedSegmentMax unsortedSegmentMax(Operand data,
      Operand segmentIds, Operand numSegments) {
    return UnsortedSegmentMax.create(scope, data, segmentIds, numSegments);
  }

  /**
   * Adds an {@link QuantizeDownAndShrinkRange} operation to the graph
   *
   * @param input 
   * @param inputMin The float value that the minimum quantized input value represents.
   * @param inputMax The float value that the maximum quantized input value represents.
   * @param outType The type of the output. Should be a lower bit depth than Tinput.
   * @return a new instance of QuantizeDownAndShrinkRange
   * @see {@link org.tensorflow.op.core.QuantizeDownAndShrinkRange}
   */
  public  QuantizeDownAndShrinkRange quantizeDownAndShrinkRange(Operand input,
      Operand inputMin, Operand inputMax, Class outType) {
    return QuantizeDownAndShrinkRange.create(scope, input, inputMin, inputMax, outType);
  }

  /**
   * Adds an {@link SaveV2} operation to the graph
   *
   * @param prefix Must have a single element. The prefix of the V2 checkpoint to which we
   * @param tensorNames shape {N}. The names of the tensors to be saved.
   * @param shapeAndSlices shape {N}.  The slice specs of the tensors to be saved.
   * @param tensors `N` tensors to save.
   * @return a new instance of SaveV2
   * @see {@link org.tensorflow.op.core.SaveV2}
   */
  public SaveV2 saveV2(Operand prefix, Operand tensorNames,
      Operand shapeAndSlices, Iterable> tensors) {
    return SaveV2.create(scope, prefix, tensorNames, shapeAndSlices, tensors);
  }

  /**
   * Adds an {@link AddN} operation to the graph
   *
   * @param inputs Must all be the same size and shape.
   * @return a new instance of AddN
   * @see {@link org.tensorflow.op.core.AddN}
   */
  public  AddN addN(Operand inputs) {
    return AddN.create(scope, inputs);
  }

  /**
   * Adds an {@link DecodeAndCropJpeg} operation to the graph
   *
   * @param contents 0-D.  The JPEG-encoded image.
   * @param cropWindow 1-D.  The crop window: [crop_y, crop_x, crop_height, crop_width].
   * @param options carries optional attributes values
   * @return a new instance of DecodeAndCropJpeg
   * @see {@link org.tensorflow.op.core.DecodeAndCropJpeg}
   */
  public DecodeAndCropJpeg decodeAndCropJpeg(Operand contents, Operand cropWindow,
      DecodeAndCropJpeg.Options... options) {
    return DecodeAndCropJpeg.create(scope, contents, cropWindow, options);
  }

  /**
   * Adds an {@link Digamma} operation to the graph
   *
   * @param x 
   * @return a new instance of Digamma
   * @see {@link org.tensorflow.op.core.Digamma}
   */
  public  Digamma digamma(Operand x) {
    return Digamma.create(scope, x);
  }

  /**
   * Adds an {@link ShardedFilename} operation to the graph
   *
   * @param basename 
   * @param shard 
   * @param numShards 
   * @return a new instance of ShardedFilename
   * @see {@link org.tensorflow.op.core.ShardedFilename}
   */
  public ShardedFilename shardedFilename(Operand basename, Operand shard,
      Operand numShards) {
    return ShardedFilename.create(scope, basename, shard, numShards);
  }

  /**
   * Adds an {@link SparseApplyAdagrad} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param lr Learning rate. Must be a scalar.
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param options carries optional attributes values
   * @return a new instance of SparseApplyAdagrad
   * @see {@link org.tensorflow.op.core.SparseApplyAdagrad}
   */
  public  SparseApplyAdagrad sparseApplyAdagrad(Operand var,
      Operand accum, Operand lr, Operand grad, Operand indices,
      SparseApplyAdagrad.Options... options) {
    return SparseApplyAdagrad.create(scope, var, accum, lr, grad, indices, options);
  }

  /**
   * Adds an {@link Stack} operation to the graph
   *
   * @param values Must be of same shape and type.
   * @param options carries optional attributes values
   * @return a new instance of Stack
   * @see {@link org.tensorflow.op.core.Stack}
   */
  public  Stack stack(Operand values, Stack.Options... options) {
    return Stack.create(scope, values, options);
  }

  /**
   * Adds an {@link StridedSliceGrad} operation to the graph
   *
   * @param shape 
   * @param begin 
   * @param end 
   * @param strides 
   * @param dy 
   * @param options carries optional attributes values
   * @return a new instance of StridedSliceGrad
   * @see {@link org.tensorflow.op.core.StridedSliceGrad}
   */
  public  StridedSliceGrad stridedSliceGrad(Operand shape,
      Operand begin, Operand end, Operand strides, Operand dy,
      StridedSliceGrad.Options... options) {
    return StridedSliceGrad.create(scope, shape, begin, end, strides, dy, options);
  }

  /**
   * Adds an {@link DeepCopy} operation to the graph
   *
   * @param x The source tensor of type `T`.
   * @return a new instance of DeepCopy
   * @see {@link org.tensorflow.op.core.DeepCopy}
   */
  public  DeepCopy deepCopy(Operand x) {
    return DeepCopy.create(scope, x);
  }

  /**
   * Adds an {@link Multinomial} operation to the graph
   *
   * @param logits 2-D Tensor with shape `[batch_size, num_classes]`.  Each slice `[i, :]`
   * @param numSamples 0-D.  Number of independent samples to draw for each row slice.
   * @param outputDtype 
   * @param options carries optional attributes values
   * @return a new instance of Multinomial
   * @see {@link org.tensorflow.op.core.Multinomial}
   */
  public  Multinomial multinomial(Operand logits,
      Operand numSamples, Class outputDtype, Multinomial.Options... options) {
    return Multinomial.create(scope, logits, numSamples, outputDtype, options);
  }

  /**
   * Adds an {@link QuantizeAndDequantizeV2} operation to the graph
   *
   * @param input Tensor to quantize and then dequantize.
   * @param inputMin If `range_given == True`, this specifies the minimum input value that needs to
   * @param inputMax If `range_given == True`, this specifies the maximum input value that needs to
   * @param options carries optional attributes values
   * @return a new instance of QuantizeAndDequantizeV2
   * @see {@link org.tensorflow.op.core.QuantizeAndDequantizeV2}
   */
  public  QuantizeAndDequantizeV2 quantizeAndDequantizeV2(Operand input,
      Operand inputMin, Operand inputMax, QuantizeAndDequantizeV2.Options... options) {
    return QuantizeAndDequantizeV2.create(scope, input, inputMin, inputMax, options);
  }

  /**
   * Adds an {@link InplaceUpdate} operation to the graph
   *
   * @param x A tensor of type `T`.
   * @param i A vector. Indices into the left-most dimension of `x`.
   * @param v A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size.
   * @return a new instance of InplaceUpdate
   * @see {@link org.tensorflow.op.core.InplaceUpdate}
   */
  public  InplaceUpdate inplaceUpdate(Operand x, Operand i, Operand v) {
    return InplaceUpdate.create(scope, x, i, v);
  }

  /**
   * Adds an {@link IRFFT2D} operation to the graph
   *
   * @param input A complex64 tensor.
   * @param fftLength An int32 tensor of shape [2]. The FFT length for each dimension.
   * @return a new instance of IRFFT2D
   * @see {@link org.tensorflow.op.core.IRFFT2D}
   */
  public IRFFT2D iRFFT2D(Operand input, Operand fftLength) {
    return IRFFT2D.create(scope, input, fftLength);
  }

  /**
   * Adds an {@link Unstack} operation to the graph
   *
   * @param value 1-D or higher, with `axis` dimension size equal to `num`.
   * @param num 
   * @param options carries optional attributes values
   * @return a new instance of Unstack
   * @see {@link org.tensorflow.op.core.Unstack}
   */
  public  Unstack unstack(Operand value, Long num, Unstack.Options... options) {
    return Unstack.create(scope, value, num, options);
  }

  /**
   * Adds an {@link PaddingFIFOQueue} operation to the graph
   *
   * @param componentTypes The type of each component in a value.
   * @param options carries optional attributes values
   * @return a new instance of PaddingFIFOQueue
   * @see {@link org.tensorflow.op.core.PaddingFIFOQueue}
   */
  public PaddingFIFOQueue paddingFIFOQueue(List> componentTypes,
      PaddingFIFOQueue.Options... options) {
    return PaddingFIFOQueue.create(scope, componentTypes, options);
  }

  /**
   * Adds an {@link AllCandidateSampler} operation to the graph
   *
   * @param trueClasses A batch_size * num_true matrix, in which each row contains the
   * @param numTrue Number of true labels per context.
   * @param numSampled Number of candidates to produce.
   * @param unique If unique is true, we sample with rejection, so that all sampled
   * @param options carries optional attributes values
   * @return a new instance of AllCandidateSampler
   * @see {@link org.tensorflow.op.core.AllCandidateSampler}
   */
  public AllCandidateSampler allCandidateSampler(Operand trueClasses, Long numTrue,
      Long numSampled, Boolean unique, AllCandidateSampler.Options... options) {
    return AllCandidateSampler.create(scope, trueClasses, numTrue, numSampled, unique, options);
  }

  /**
   * Adds an {@link Where} operation to the graph
   *
   * @param condition 
   * @return a new instance of Where
   * @see {@link org.tensorflow.op.core.Where}
   */
  public  Where where(Operand condition) {
    return Where.create(scope, condition);
  }

  /**
   * Adds an {@link Gradients} operation to the graph
   *
   * @param y outputs of the function to derive
   * @param x inputs of the function for which partial derivatives are computed
   * @param options carries optional attributes values
   * @return a new instance of {@code Gradients}
   * @see {@link org.tensorflow.op.core.Gradients}
   */
  public Gradients gradients(Iterable> y, Iterable> x,
      Gradients.Options... options) {
    return Gradients.create(scope, y, x, options);
  }

  /**
   * Adds an {@link Min} operation to the graph
   *
   * @param input The tensor to reduce.
   * @param axis The dimensions to reduce. Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of Min
   * @see {@link org.tensorflow.op.core.Min}
   */
  public  Min min(Operand input, Operand axis,
      Min.Options... options) {
    return Min.create(scope, input, axis, options);
  }

  /**
   * Adds an {@link UnsortedSegmentMin} operation to the graph
   *
   * @param data 
   * @param segmentIds A 1-D tensor whose rank is equal to the rank of `data`'s
   * @param numSegments 
   * @return a new instance of UnsortedSegmentMin
   * @see {@link org.tensorflow.op.core.UnsortedSegmentMin}
   */
  public  UnsortedSegmentMin unsortedSegmentMin(Operand data,
      Operand segmentIds, Operand numSegments) {
    return UnsortedSegmentMin.create(scope, data, segmentIds, numSegments);
  }

  /**
   * Adds an {@link Conv3D} operation to the graph
   *
   * @param input Shape `[batch, in_depth, in_height, in_width, in_channels]`.
   * @param filter Shape `[filter_depth, filter_height, filter_width, in_channels,
   * @param strides 1-D tensor of length 5. The stride of the sliding window for each
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of Conv3D
   * @see {@link org.tensorflow.op.core.Conv3D}
   */
  public  Conv3D conv3D(Operand input, Operand filter,
      List strides, String padding, Conv3D.Options... options) {
    return Conv3D.create(scope, input, filter, strides, padding, options);
  }

  /**
   * Adds an {@link PrefetchDataset} operation to the graph
   *
   * @param inputDataset 
   * @param bufferSize The maximum number of elements to buffer in an iterator over
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of PrefetchDataset
   * @see {@link org.tensorflow.op.core.PrefetchDataset}
   */
  public PrefetchDataset prefetchDataset(Operand inputDataset, Operand bufferSize,
      List> outputTypes, List outputShapes) {
    return PrefetchDataset.create(scope, inputDataset, bufferSize, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link ReadVariableOp} operation to the graph
   *
   * @param resource handle to the resource in which to store the variable.
   * @param dtype the dtype of the value.
   * @return a new instance of ReadVariableOp
   * @see {@link org.tensorflow.op.core.ReadVariableOp}
   */
  public  ReadVariableOp readVariableOp(Operand resource, Class dtype) {
    return ReadVariableOp.create(scope, resource, dtype);
  }

  /**
   * Adds an {@link MaxPoolGradGrad} operation to the graph
   *
   * @param origInput The original input tensor.
   * @param origOutput The original output tensor.
   * @param grad 4-D.  Gradients of gradients w.r.t. the input of `max_pool`.
   * @param ksize The size of the window for each dimension of the input tensor.
   * @param strides The stride of the sliding window for each dimension of the
   * @param padding The type of padding algorithm to use.
   * @param options carries optional attributes values
   * @return a new instance of MaxPoolGradGrad
   * @see {@link org.tensorflow.op.core.MaxPoolGradGrad}
   */
  public  MaxPoolGradGrad maxPoolGradGrad(Operand origInput,
      Operand origOutput, Operand grad, List ksize, List strides, String padding,
      MaxPoolGradGrad.Options... options) {
    return MaxPoolGradGrad.create(scope, origInput, origOutput, grad, ksize, strides, padding, options);
  }

  /**
   * Adds an {@link ReadFile} operation to the graph
   *
   * @param filename 
   * @return a new instance of ReadFile
   * @see {@link org.tensorflow.op.core.ReadFile}
   */
  public ReadFile readFile(Operand filename) {
    return ReadFile.create(scope, filename);
  }

  /**
   * Adds an {@link DataFormatVecPermute} operation to the graph
   *
   * @param x Vector of size 4 or Tensor of shape (4, 2) in source data format.
   * @param options carries optional attributes values
   * @return a new instance of DataFormatVecPermute
   * @see {@link org.tensorflow.op.core.DataFormatVecPermute}
   */
  public  DataFormatVecPermute dataFormatVecPermute(Operand x,
      DataFormatVecPermute.Options... options) {
    return DataFormatVecPermute.create(scope, x, options);
  }

  /**
   * Adds an {@link QuantizeAndDequantize} operation to the graph
   *
   * @param input 
   * @param options carries optional attributes values
   * @return a new instance of QuantizeAndDequantize
   * @see {@link org.tensorflow.op.core.QuantizeAndDequantize}
   */
  public  QuantizeAndDequantize quantizeAndDequantize(Operand input,
      QuantizeAndDequantize.Options... options) {
    return QuantizeAndDequantize.create(scope, input, options);
  }

  /**
   * Adds an {@link MutexLock} operation to the graph
   *
   * @param mutex The mutex resource to lock.
   * @return a new instance of MutexLock
   * @see {@link org.tensorflow.op.core.MutexLock}
   */
  public MutexLock mutexLock(Operand mutex) {
    return MutexLock.create(scope, mutex);
  }

  /**
   * Adds an {@link SkipDataset} operation to the graph
   *
   * @param inputDataset 
   * @param count A scalar representing the number of elements from the `input_dataset`
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of SkipDataset
   * @see {@link org.tensorflow.op.core.SkipDataset}
   */
  public SkipDataset skipDataset(Operand inputDataset, Operand count,
      List> outputTypes, List outputShapes) {
    return SkipDataset.create(scope, inputDataset, count, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link TensorSummaryV2} operation to the graph
   *
   * @param tag A string attached to this summary. Used for organization in TensorBoard.
   * @param tensor A tensor to serialize.
   * @param serializedSummaryMetadata A serialized SummaryMetadata proto. Contains plugin
   * @return a new instance of TensorSummaryV2
   * @see {@link org.tensorflow.op.core.TensorSummaryV2}
   */
  public  TensorSummaryV2 tensorSummaryV2(Operand tag, Operand tensor,
      Operand serializedSummaryMetadata) {
    return TensorSummaryV2.create(scope, tag, tensor, serializedSummaryMetadata);
  }

  /**
   * Adds an {@link ApplyAdam} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param m Should be from a Variable().
   * @param v Should be from a Variable().
   * @param beta1Power Must be a scalar.
   * @param beta2Power Must be a scalar.
   * @param lr Scaling factor. Must be a scalar.
   * @param beta1 Momentum factor. Must be a scalar.
   * @param beta2 Momentum factor. Must be a scalar.
   * @param epsilon Ridge term. Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ApplyAdam
   * @see {@link org.tensorflow.op.core.ApplyAdam}
   */
  public  ApplyAdam applyAdam(Operand var, Operand m, Operand v,
      Operand beta1Power, Operand beta2Power, Operand lr, Operand beta1,
      Operand beta2, Operand epsilon, Operand grad, ApplyAdam.Options... options) {
    return ApplyAdam.create(scope, var, m, v, beta1Power, beta2Power, lr, beta1, beta2, epsilon, grad, options);
  }

  /**
   * Adds an {@link Bincount} operation to the graph
   *
   * @param arr int32 `Tensor`.
   * @param size non-negative int32 scalar `Tensor`.
   * @param weights is an int32, int64, float32, or float64 `Tensor` with the same
   * @return a new instance of Bincount
   * @see {@link org.tensorflow.op.core.Bincount}
   */
  public  Bincount bincount(Operand arr, Operand size,
      Operand weights) {
    return Bincount.create(scope, arr, size, weights);
  }

  /**
   * Adds an {@link ReduceAll} operation to the graph
   *
   * @param input The tensor to reduce.
   * @param axis The dimensions to reduce. Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of ReduceAll
   * @see {@link org.tensorflow.op.core.ReduceAll}
   */
  public  ReduceAll reduceAll(Operand input, Operand axis,
      ReduceAll.Options... options) {
    return ReduceAll.create(scope, input, axis, options);
  }

  /**
   * Adds an {@link TensorListElementShape} operation to the graph
   *
   * @param inputHandle 
   * @param shapeType 
   * @return a new instance of TensorListElementShape
   * @see {@link org.tensorflow.op.core.TensorListElementShape}
   */
  public  TensorListElementShape tensorListElementShape(Operand inputHandle,
      Class shapeType) {
    return TensorListElementShape.create(scope, inputHandle, shapeType);
  }

  /**
   * Adds an {@link RandomUniformInt} operation to the graph
   *
   * @param shape The shape of the output tensor.
   * @param minval 0-D.  Inclusive lower bound on the generated integers.
   * @param maxval 0-D.  Exclusive upper bound on the generated integers.
   * @param options carries optional attributes values
   * @return a new instance of RandomUniformInt
   * @see {@link org.tensorflow.op.core.RandomUniformInt}
   */
  public  RandomUniformInt randomUniformInt(Operand shape,
      Operand minval, Operand maxval, RandomUniformInt.Options... options) {
    return RandomUniformInt.create(scope, shape, minval, maxval, options);
  }

  /**
   * Adds an {@link SampleDistortedBoundingBoxV2} operation to the graph
   *
   * @param imageSize 1-D, containing `[height, width, channels]`.
   * @param boundingBoxes 3-D with shape `[batch, N, 4]` describing the N bounding boxes
   * @param minObjectCovered The cropped area of the image must contain at least this
   * @param options carries optional attributes values
   * @return a new instance of SampleDistortedBoundingBoxV2
   * @see {@link org.tensorflow.op.core.SampleDistortedBoundingBoxV2}
   */
  public  SampleDistortedBoundingBoxV2 sampleDistortedBoundingBoxV2(Operand imageSize,
      Operand boundingBoxes, Operand minObjectCovered,
      SampleDistortedBoundingBoxV2.Options... options) {
    return SampleDistortedBoundingBoxV2.create(scope, imageSize, boundingBoxes, minObjectCovered, options);
  }

  /**
   * Adds an {@link Fill} operation to the graph
   *
   * @param dims 1-D. Represents the shape of the output tensor.
   * @param value 0-D (scalar). Value to fill the returned tensor.
   * @return a new instance of Fill
   * @see {@link org.tensorflow.op.core.Fill}
   */
  public  Fill fill(Operand dims, Operand value) {
    return Fill.create(scope, dims, value);
  }

  /**
   * Adds an {@link StageSize} operation to the graph
   *
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of StageSize
   * @see {@link org.tensorflow.op.core.StageSize}
   */
  public StageSize stageSize(List> dtypes, StageSize.Options... options) {
    return StageSize.create(scope, dtypes, options);
  }

  /**
   * Adds an {@link Maximum} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of Maximum
   * @see {@link org.tensorflow.op.core.Maximum}
   */
  public  Maximum maximum(Operand x, Operand y) {
    return Maximum.create(scope, x, y);
  }

  /**
   * Adds an {@link TensorArray} operation to the graph
   *
   * @param size The size of the array.
   * @param dtype The type of the elements on the tensor_array.
   * @param options carries optional attributes values
   * @return a new instance of TensorArray
   * @see {@link org.tensorflow.op.core.TensorArray}
   */
  public  TensorArray tensorArray(Operand size, Class dtype,
      TensorArray.Options... options) {
    return TensorArray.create(scope, size, dtype, options);
  }

  /**
   * Adds an {@link Log1p} operation to the graph
   *
   * @param x 
   * @return a new instance of Log1p
   * @see {@link org.tensorflow.op.core.Log1p}
   */
  public  Log1p log1p(Operand x) {
    return Log1p.create(scope, x);
  }

  /**
   * Adds an {@link Constant} operation to the graph
   *
   * @param shape the tensor shape.
   * @param data a buffer containing the tensor data.
   * @throws IllegalArgumentException If the tensor shape is not compatible with the buffer
   * @see {@link org.tensorflow.op.core.Constant}
   */
  public Constant constant(long[] shape, LongBuffer data) {
    return Constant.create(scope, shape, data);
  }

  /**
   * Adds an {@link QuantizedMul} operation to the graph
   *
   * @param x 
   * @param y 
   * @param minX The float value that the lowest quantized `x` value represents.
   * @param maxX The float value that the highest quantized `x` value represents.
   * @param minY The float value that the lowest quantized `y` value represents.
   * @param maxY The float value that the highest quantized `y` value represents.
   * @param Toutput 
   * @return a new instance of QuantizedMul
   * @see {@link org.tensorflow.op.core.QuantizedMul}
   */
  public  QuantizedMul quantizedMul(Operand x, Operand y, Operand minX,
      Operand maxX, Operand minY, Operand maxY, Class Toutput) {
    return QuantizedMul.create(scope, x, y, minX, maxX, minY, maxY, Toutput);
  }

  /**
   * Adds an {@link ResourceGather} operation to the graph
   *
   * @param resource 
   * @param indices 
   * @param dtype 
   * @param options carries optional attributes values
   * @return a new instance of ResourceGather
   * @see {@link org.tensorflow.op.core.ResourceGather}
   */
  public  ResourceGather resourceGather(Operand resource,
      Operand indices, Class dtype, ResourceGather.Options... options) {
    return ResourceGather.create(scope, resource, indices, dtype, options);
  }

  /**
   * Adds an {@link InplaceSub} operation to the graph
   *
   * @param x A `Tensor` of type T.
   * @param i A vector. Indices into the left-most dimension of `x`.
   * @param v A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size.
   * @return a new instance of InplaceSub
   * @see {@link org.tensorflow.op.core.InplaceSub}
   */
  public  InplaceSub inplaceSub(Operand x, Operand i, Operand v) {
    return InplaceSub.create(scope, x, i, v);
  }

  /**
   * Adds an {@link SparseMatMul} operation to the graph
   *
   * @param a 
   * @param b 
   * @param options carries optional attributes values
   * @return a new instance of SparseMatMul
   * @see {@link org.tensorflow.op.core.SparseMatMul}
   */
  public  SparseMatMul sparseMatMul(Operand a, Operand b,
      SparseMatMul.Options... options) {
    return SparseMatMul.create(scope, a, b, options);
  }

  /**
   * Adds an {@link TensorArrayRead} operation to the graph
   *
   * @param handle The handle to a TensorArray.
   * @param index 
   * @param flowIn A float scalar that enforces proper chaining of operations.
   * @param dtype The type of the elem that is returned.
   * @return a new instance of TensorArrayRead
   * @see {@link org.tensorflow.op.core.TensorArrayRead}
   */
  public  TensorArrayRead tensorArrayRead(Operand handle, Operand index,
      Operand flowIn, Class dtype) {
    return TensorArrayRead.create(scope, handle, index, flowIn, dtype);
  }

  /**
   * Adds an {@link RandomDataset} operation to the graph
   *
   * @param seed A scalar seed for the random number generator. If either seed or
   * @param seed2 A second scalar seed to avoid seed collision.
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of RandomDataset
   * @see {@link org.tensorflow.op.core.RandomDataset}
   */
  public RandomDataset randomDataset(Operand seed, Operand seed2,
      List> outputTypes, List outputShapes) {
    return RandomDataset.create(scope, seed, seed2, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link OrderedMapStage} operation to the graph
   *
   * @param key int64
   * @param indices 
   * @param values a list of tensors
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of OrderedMapStage
   * @see {@link org.tensorflow.op.core.OrderedMapStage}
   */
  public OrderedMapStage orderedMapStage(Operand key, Operand indices,
      Iterable> values, List> dtypes, OrderedMapStage.Options... options) {
    return OrderedMapStage.create(scope, key, indices, values, dtypes, options);
  }

  /**
   * Adds an {@link MapClear} operation to the graph
   *
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of MapClear
   * @see {@link org.tensorflow.op.core.MapClear}
   */
  public MapClear mapClear(List> dtypes, MapClear.Options... options) {
    return MapClear.create(scope, dtypes, options);
  }

  /**
   * Adds an {@link RecordInput} operation to the graph
   *
   * @param filePattern Glob pattern for the data files.
   * @param options carries optional attributes values
   * @return a new instance of RecordInput
   * @see {@link org.tensorflow.op.core.RecordInput}
   */
  public RecordInput recordInput(String filePattern, RecordInput.Options... options) {
    return RecordInput.create(scope, filePattern, options);
  }

  /**
   * Adds an {@link ResourceApplyAddSign} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param m Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param alpha Must be a scalar.
   * @param signDecay Must be a scalar.
   * @param beta Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ResourceApplyAddSign
   * @see {@link org.tensorflow.op.core.ResourceApplyAddSign}
   */
  public  ResourceApplyAddSign resourceApplyAddSign(Operand var, Operand m, Operand lr,
      Operand alpha, Operand signDecay, Operand beta, Operand grad,
      ResourceApplyAddSign.Options... options) {
    return ResourceApplyAddSign.create(scope, var, m, lr, alpha, signDecay, beta, grad, options);
  }

  /**
   * Adds an {@link MutableHashTable} operation to the graph
   *
   * @param keyDtype Type of the table keys.
   * @param valueDtype Type of the table values.
   * @param options carries optional attributes values
   * @return a new instance of MutableHashTable
   * @see {@link org.tensorflow.op.core.MutableHashTable}
   */
  public  MutableHashTable mutableHashTable(Class keyDtype, Class valueDtype,
      MutableHashTable.Options... options) {
    return MutableHashTable.create(scope, keyDtype, valueDtype, options);
  }

  /**
   * Adds an {@link MatrixBandPart} operation to the graph
   *
   * @param input Rank `k` tensor.
   * @param numLower 0-D tensor. Number of subdiagonals to keep. If negative, keep entire
   * @param numUpper 0-D tensor. Number of superdiagonals to keep. If negative, keep
   * @return a new instance of MatrixBandPart
   * @see {@link org.tensorflow.op.core.MatrixBandPart}
   */
  public  MatrixBandPart matrixBandPart(Operand input,
      Operand numLower, Operand numUpper) {
    return MatrixBandPart.create(scope, input, numLower, numUpper);
  }

  /**
   * Adds an {@link Fact} operation to the graph
   *
   * @return a new instance of Fact
   * @see {@link org.tensorflow.op.core.Fact}
   */
  public Fact fact() {
    return Fact.create(scope);
  }

  /**
   * Adds an {@link TensorListPushBack} operation to the graph
   *
   * @param inputHandle 
   * @param tensor 
   * @return a new instance of TensorListPushBack
   * @see {@link org.tensorflow.op.core.TensorListPushBack}
   */
  public  TensorListPushBack tensorListPushBack(Operand inputHandle, Operand tensor) {
    return TensorListPushBack.create(scope, inputHandle, tensor);
  }

  /**
   * Adds an {@link RemoteFusedGraphExecute} operation to the graph
   *
   * @param inputs Arbitrary number of tensors with arbitrary data types
   * @param Toutputs 
   * @param serializedRemoteFusedGraphExecuteInfo Serialized protocol buffer
   * @return a new instance of RemoteFusedGraphExecute
   * @see {@link org.tensorflow.op.core.RemoteFusedGraphExecute}
   */
  public RemoteFusedGraphExecute remoteFusedGraphExecute(Iterable> inputs,
      List> Toutputs, String serializedRemoteFusedGraphExecuteInfo) {
    return RemoteFusedGraphExecute.create(scope, inputs, Toutputs, serializedRemoteFusedGraphExecuteInfo);
  }

  /**
   * Adds an {@link ResourceSparseApplyProximalGradientDescent} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param alpha Scaling factor. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 regularization. Must be a scalar.
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param options carries optional attributes values
   * @return a new instance of ResourceSparseApplyProximalGradientDescent
   * @see {@link org.tensorflow.op.core.ResourceSparseApplyProximalGradientDescent}
   */
  public  ResourceSparseApplyProximalGradientDescent resourceSparseApplyProximalGradientDescent(Operand var,
      Operand alpha, Operand l1, Operand l2, Operand grad, Operand indices,
      ResourceSparseApplyProximalGradientDescent.Options... options) {
    return ResourceSparseApplyProximalGradientDescent.create(scope, var, alpha, l1, l2, grad, indices, options);
  }

  /**
   * Adds an {@link UnbatchGrad} operation to the graph
   *
   * @param originalInput 
   * @param batchIndex 
   * @param grad 
   * @param id 
   * @param options carries optional attributes values
   * @return a new instance of UnbatchGrad
   * @see {@link org.tensorflow.op.core.UnbatchGrad}
   */
  public  UnbatchGrad unbatchGrad(Operand originalInput, Operand batchIndex,
      Operand grad, Operand id, UnbatchGrad.Options... options) {
    return UnbatchGrad.create(scope, originalInput, batchIndex, grad, id, options);
  }

  /**
   * Adds an {@link VarHandleOp} operation to the graph
   *
   * @param dtype the type of this variable. Must agree with the dtypes
   * @param shape The (possibly partially specified) shape of this variable.
   * @param options carries optional attributes values
   * @return a new instance of VarHandleOp
   * @see {@link org.tensorflow.op.core.VarHandleOp}
   */
  public  VarHandleOp varHandleOp(Class dtype, Shape shape, VarHandleOp.Options... options) {
    return VarHandleOp.create(scope, dtype, shape, options);
  }

  /**
   * Adds an {@link Floor} operation to the graph
   *
   * @param x 
   * @return a new instance of Floor
   * @see {@link org.tensorflow.op.core.Floor}
   */
  public  Floor floor(Operand x) {
    return Floor.create(scope, x);
  }

  /**
   * Adds an {@link IRFFT} operation to the graph
   *
   * @param input A complex64 tensor.
   * @param fftLength An int32 tensor of shape [1]. The FFT length.
   * @return a new instance of IRFFT
   * @see {@link org.tensorflow.op.core.IRFFT}
   */
  public IRFFT iRFFT(Operand input, Operand fftLength) {
    return IRFFT.create(scope, input, fftLength);
  }

  /**
   * Adds an {@link ResizeBicubic} operation to the graph
   *
   * @param images 4-D with shape `[batch, height, width, channels]`.
   * @param size = A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
   * @param options carries optional attributes values
   * @return a new instance of ResizeBicubic
   * @see {@link org.tensorflow.op.core.ResizeBicubic}
   */
  public  ResizeBicubic resizeBicubic(Operand images, Operand size,
      ResizeBicubic.Options... options) {
    return ResizeBicubic.create(scope, images, size, options);
  }

  /**
   * Adds an {@link ApplyFtrl} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param accum Should be from a Variable().
   * @param linear Should be from a Variable().
   * @param grad The gradient.
   * @param lr Scaling factor. Must be a scalar.
   * @param l1 L1 regulariation. Must be a scalar.
   * @param l2 L2 regulariation. Must be a scalar.
   * @param lrPower Scaling factor. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of ApplyFtrl
   * @see {@link org.tensorflow.op.core.ApplyFtrl}
   */
  public  ApplyFtrl applyFtrl(Operand var, Operand accum, Operand linear,
      Operand grad, Operand lr, Operand l1, Operand l2, Operand lrPower,
      ApplyFtrl.Options... options) {
    return ApplyFtrl.create(scope, var, accum, linear, grad, lr, l1, l2, lrPower, options);
  }

  /**
   * Adds an {@link ConditionalAccumulator} operation to the graph
   *
   * @param dtype The type of the value being accumulated.
   * @param shape The shape of the values, can be [], in which case shape is unknown.
   * @param options carries optional attributes values
   * @return a new instance of ConditionalAccumulator
   * @see {@link org.tensorflow.op.core.ConditionalAccumulator}
   */
  public  ConditionalAccumulator conditionalAccumulator(Class dtype, Shape shape,
      ConditionalAccumulator.Options... options) {
    return ConditionalAccumulator.create(scope, dtype, shape, options);
  }

  /**
   * Adds an {@link TemporaryVariable} operation to the graph
   *
   * @param shape The shape of the variable tensor.
   * @param dtype The type of elements in the variable tensor.
   * @param options carries optional attributes values
   * @return a new instance of TemporaryVariable
   * @see {@link org.tensorflow.op.core.TemporaryVariable}
   */
  public  TemporaryVariable temporaryVariable(Shape shape, Class dtype,
      TemporaryVariable.Options... options) {
    return TemporaryVariable.create(scope, shape, dtype, options);
  }

  /**
   * Adds an {@link RandomNormal} operation to the graph
   *
   * @param shape The shape of the output tensor.
   * @param dtype The type of the output.
   * @param options carries optional attributes values
   * @return a new instance of RandomNormal
   * @see {@link org.tensorflow.op.core.RandomNormal}
   */
  public  RandomNormal randomNormal(Operand shape,
      Class dtype, RandomNormal.Options... options) {
    return RandomNormal.create(scope, shape, dtype, options);
  }

  /**
   * Adds an {@link InvertPermutation} operation to the graph
   *
   * @param x 1-D.
   * @return a new instance of InvertPermutation
   * @see {@link org.tensorflow.op.core.InvertPermutation}
   */
  public  InvertPermutation invertPermutation(Operand x) {
    return InvertPermutation.create(scope, x);
  }

  /**
   * Adds an {@link MatrixDiagPart} operation to the graph
   *
   * @param input Rank `k` tensor where `k >= 2`.
   * @return a new instance of MatrixDiagPart
   * @see {@link org.tensorflow.op.core.MatrixDiagPart}
   */
  public  MatrixDiagPart matrixDiagPart(Operand input) {
    return MatrixDiagPart.create(scope, input);
  }

  /**
   * Adds an {@link MakeIterator} operation to the graph
   *
   * @param dataset 
   * @param iterator 
   * @return a new instance of MakeIterator
   * @see {@link org.tensorflow.op.core.MakeIterator}
   */
  public MakeIterator makeIterator(Operand dataset, Operand iterator) {
    return MakeIterator.create(scope, dataset, iterator);
  }

  /**
   * Adds an {@link DebugGradientRefIdentity} operation to the graph
   *
   * @param input 
   * @return a new instance of DebugGradientRefIdentity
   * @see {@link org.tensorflow.op.core.DebugGradientRefIdentity}
   */
  public  DebugGradientRefIdentity debugGradientRefIdentity(Operand input) {
    return DebugGradientRefIdentity.create(scope, input);
  }

  /**
   * Adds an {@link MapUnstageNoKey} operation to the graph
   *
   * @param indices 
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of MapUnstageNoKey
   * @see {@link org.tensorflow.op.core.MapUnstageNoKey}
   */
  public MapUnstageNoKey mapUnstageNoKey(Operand indices, List> dtypes,
      MapUnstageNoKey.Options... options) {
    return MapUnstageNoKey.create(scope, indices, dtypes, options);
  }

  /**
   * Adds an {@link ReduceMax} operation to the graph
   *
   * @param input The tensor to reduce.
   * @param axis The dimensions to reduce. Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of ReduceMax
   * @see {@link org.tensorflow.op.core.ReduceMax}
   */
  public  ReduceMax reduceMax(Operand input, Operand axis,
      ReduceMax.Options... options) {
    return ReduceMax.create(scope, input, axis, options);
  }

  /**
   * Adds an {@link ResourceScatterNdAdd} operation to the graph
   *
   * @param ref A resource handle. Must be from a VarHandleOp.
   * @param indices A Tensor. Must be one of the following types: int32, int64.
   * @param updates A Tensor. Must have the same type as ref. A tensor of
   * @param options carries optional attributes values
   * @return a new instance of ResourceScatterNdAdd
   * @see {@link org.tensorflow.op.core.ResourceScatterNdAdd}
   */
  public  ResourceScatterNdAdd resourceScatterNdAdd(Operand ref,
      Operand indices, Operand updates, ResourceScatterNdAdd.Options... options) {
    return ResourceScatterNdAdd.create(scope, ref, indices, updates, options);
  }

  /**
   * Adds an {@link RFFT} operation to the graph
   *
   * @param input A float32 tensor.
   * @param fftLength An int32 tensor of shape [1]. The FFT length.
   * @return a new instance of RFFT
   * @see {@link org.tensorflow.op.core.RFFT}
   */
  public RFFT rFFT(Operand input, Operand fftLength) {
    return RFFT.create(scope, input, fftLength);
  }

  /**
   * Adds an {@link Svd} operation to the graph
   *
   * @param input A tensor of shape `[..., M, N]` whose inner-most 2 dimensions
   * @param options carries optional attributes values
   * @return a new instance of Svd
   * @see {@link org.tensorflow.op.core.Svd}
   */
  public  Svd svd(Operand input, Svd.Options... options) {
    return Svd.create(scope, input, options);
  }

  /**
   * Adds an {@link GetSessionHandle} operation to the graph
   *
   * @param value The tensor to be stored.
   * @return a new instance of GetSessionHandle
   * @see {@link org.tensorflow.op.core.GetSessionHandle}
   */
  public  GetSessionHandle getSessionHandle(Operand value) {
    return GetSessionHandle.create(scope, value);
  }

  /**
   * Adds an {@link Subtract} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of Subtract
   * @see {@link org.tensorflow.op.core.Subtract}
   */
  public  Subtract subtract(Operand x, Operand y) {
    return Subtract.create(scope, x, y);
  }

  /**
   * Adds an {@link BatchIFFT3D} operation to the graph
   *
   * @param input 
   * @return a new instance of BatchIFFT3D
   * @see {@link org.tensorflow.op.core.BatchIFFT3D}
   */
  public BatchIFFT3D batchIFFT3D(Operand input) {
    return BatchIFFT3D.create(scope, input);
  }

  /**
   * Adds an {@link BatchNormWithGlobalNormalization} operation to the graph
   *
   * @param t A 4D input Tensor.
   * @param m A 1D mean Tensor with size matching the last dimension of t.
   * @param v A 1D variance Tensor with size matching the last dimension of t.
   * @param beta A 1D beta Tensor with size matching the last dimension of t.
   * @param gamma A 1D gamma Tensor with size matching the last dimension of t.
   * @param varianceEpsilon A small float number to avoid dividing by 0.
   * @param scaleAfterNormalization A bool indicating whether the resulted tensor
   * @return a new instance of BatchNormWithGlobalNormalization
   * @see {@link org.tensorflow.op.core.BatchNormWithGlobalNormalization}
   */
  public  BatchNormWithGlobalNormalization batchNormWithGlobalNormalization(Operand t,
      Operand m, Operand v, Operand beta, Operand gamma, Float varianceEpsilon,
      Boolean scaleAfterNormalization) {
    return BatchNormWithGlobalNormalization.create(scope, t, m, v, beta, gamma, varianceEpsilon, scaleAfterNormalization);
  }

  /**
   * Adds an {@link SdcaOptimizer} operation to the graph
   *
   * @param sparseExampleIndices a list of vectors which contain example indices.
   * @param sparseFeatureIndices a list of vectors which contain feature indices.
   * @param sparseFeatureValues a list of vectors which contains feature value
   * @param denseFeatures a list of matrices which contains the dense feature values.
   * @param exampleWeights a vector which contains the weight associated with each
   * @param exampleLabels a vector which contains the label/target associated with each
   * @param sparseIndices a list of vectors where each value is the indices which has
   * @param sparseWeights a list of vectors where each value is the weight associated with
   * @param denseWeights a list of vectors where the values are the weights associated
   * @param exampleStateData a list of vectors containing the example state data.
   * @param lossType Type of the primal loss. Currently SdcaSolver supports logistic,
   * @param l1 Symmetric l1 regularization strength.
   * @param l2 Symmetric l2 regularization strength.
   * @param numLossPartitions Number of partitions of the global loss function.
   * @param numInnerIterations Number of iterations per mini-batch.
   * @param options carries optional attributes values
   * @return a new instance of SdcaOptimizer
   * @see {@link org.tensorflow.op.core.SdcaOptimizer}
   */
  public SdcaOptimizer sdcaOptimizer(Iterable> sparseExampleIndices,
      Iterable> sparseFeatureIndices, Iterable> sparseFeatureValues,
      Iterable> denseFeatures, Operand exampleWeights,
      Operand exampleLabels, Iterable> sparseIndices,
      Iterable> sparseWeights, Iterable> denseWeights,
      Operand exampleStateData, String lossType, Float l1, Float l2, Long numLossPartitions,
      Long numInnerIterations, SdcaOptimizer.Options... options) {
    return SdcaOptimizer.create(scope, sparseExampleIndices, sparseFeatureIndices, sparseFeatureValues, denseFeatures, exampleWeights, exampleLabels, sparseIndices, sparseWeights, denseWeights, exampleStateData, lossType, l1, l2, numLossPartitions, numInnerIterations, options);
  }

  /**
   * Adds an {@link ResourceSparseApplyCenteredRMSProp} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param mg Should be from a Variable().
   * @param ms Should be from a Variable().
   * @param mom Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param rho Decay rate. Must be a scalar.
   * @param momentum 
   * @param epsilon Ridge term. Must be a scalar.
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var, ms and mom.
   * @param options carries optional attributes values
   * @return a new instance of ResourceSparseApplyCenteredRMSProp
   * @see {@link org.tensorflow.op.core.ResourceSparseApplyCenteredRMSProp}
   */
  public  ResourceSparseApplyCenteredRMSProp resourceSparseApplyCenteredRMSProp(Operand var,
      Operand mg, Operand ms, Operand mom, Operand lr, Operand rho,
      Operand momentum, Operand epsilon, Operand grad, Operand indices,
      ResourceSparseApplyCenteredRMSProp.Options... options) {
    return ResourceSparseApplyCenteredRMSProp.create(scope, var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, options);
  }

  /**
   * Adds an {@link DeserializeManySparse} operation to the graph
   *
   * @param serializedSparse 2-D, The `N` serialized `SparseTensor` objects.
   * @param dtype The `dtype` of the serialized `SparseTensor` objects.
   * @return a new instance of DeserializeManySparse
   * @see {@link org.tensorflow.op.core.DeserializeManySparse}
   */
  public  DeserializeManySparse deserializeManySparse(Operand serializedSparse,
      Class dtype) {
    return DeserializeManySparse.create(scope, serializedSparse, dtype);
  }

  /**
   * Adds an {@link CudnnRNNParamsSize} operation to the graph
   *
   * @param numLayers 
   * @param numUnits 
   * @param inputSize 
   * @param T 
   * @param S 
   * @param options carries optional attributes values
   * @return a new instance of CudnnRNNParamsSize
   * @see {@link org.tensorflow.op.core.CudnnRNNParamsSize}
   */
  public  CudnnRNNParamsSize cudnnRNNParamsSize(Operand numLayers,
      Operand numUnits, Operand inputSize, Class T, Class S,
      CudnnRNNParamsSize.Options... options) {
    return CudnnRNNParamsSize.create(scope, numLayers, numUnits, inputSize, T, S, options);
  }

  /**
   * Adds an {@link SparseSliceGrad} operation to the graph
   *
   * @param backpropValGrad 1-D. The gradient with respect to
   * @param inputIndices 2-D.  The `indices` of the input `SparseTensor`.
   * @param inputStart 1-D. tensor represents the start of the slice.
   * @param outputIndices 2-D.  The `indices` of the sliced `SparseTensor`.
   * @return a new instance of SparseSliceGrad
   * @see {@link org.tensorflow.op.core.SparseSliceGrad}
   */
  public  SparseSliceGrad sparseSliceGrad(Operand backpropValGrad,
      Operand inputIndices, Operand inputStart, Operand outputIndices) {
    return SparseSliceGrad.create(scope, backpropValGrad, inputIndices, inputStart, outputIndices);
  }

  /**
   * Adds an {@link ApplyCenteredRMSProp} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param mg Should be from a Variable().
   * @param ms Should be from a Variable().
   * @param mom Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param rho Decay rate. Must be a scalar.
   * @param momentum 
   * @param epsilon Ridge term. Must be a scalar.
   * @param grad The gradient.
   * @param options carries optional attributes values
   * @return a new instance of ApplyCenteredRMSProp
   * @see {@link org.tensorflow.op.core.ApplyCenteredRMSProp}
   */
  public  ApplyCenteredRMSProp applyCenteredRMSProp(Operand var, Operand mg,
      Operand ms, Operand mom, Operand lr, Operand rho, Operand momentum,
      Operand epsilon, Operand grad, ApplyCenteredRMSProp.Options... options) {
    return ApplyCenteredRMSProp.create(scope, var, mg, ms, mom, lr, rho, momentum, epsilon, grad, options);
  }

  /**
   * Adds an {@link SlideDataset} operation to the graph
   *
   * @param inputDataset 
   * @param windowSize A scalar representing the number of elements in the
   * @param stride A scalar representing the steps moving the sliding window
   * @param outputTypes 
   * @param outputShapes 
   * @return a new instance of SlideDataset
   * @see {@link org.tensorflow.op.core.SlideDataset}
   */
  public SlideDataset slideDataset(Operand inputDataset, Operand windowSize,
      Operand stride, List> outputTypes, List outputShapes) {
    return SlideDataset.create(scope, inputDataset, windowSize, stride, outputTypes, outputShapes);
  }

  /**
   * Adds an {@link FFT} operation to the graph
   *
   * @param input A complex64 tensor.
   * @return a new instance of FFT
   * @see {@link org.tensorflow.op.core.FFT}
   */
  public  FFT fFT(Operand input) {
    return FFT.create(scope, input);
  }

  /**
   * Adds an {@link ResourceScatterMin} operation to the graph
   *
   * @param resource Should be from a `Variable` node.
   * @param indices A tensor of indices into the first dimension of `ref`.
   * @param updates A tensor of updated values to add to `ref`.
   * @return a new instance of ResourceScatterMin
   * @see {@link org.tensorflow.op.core.ResourceScatterMin}
   */
  public  ResourceScatterMin resourceScatterMin(Operand resource,
      Operand indices, Operand updates) {
    return ResourceScatterMin.create(scope, resource, indices, updates);
  }

  /**
   * Adds an {@link CropAndResizeGradBoxes} operation to the graph
   *
   * @param grads A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
   * @param image A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
   * @param boxes A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor
   * @param boxInd A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
   * @param options carries optional attributes values
   * @return a new instance of CropAndResizeGradBoxes
   * @see {@link org.tensorflow.op.core.CropAndResizeGradBoxes}
   */
  public  CropAndResizeGradBoxes cropAndResizeGradBoxes(Operand grads,
      Operand image, Operand boxes, Operand boxInd,
      CropAndResizeGradBoxes.Options... options) {
    return CropAndResizeGradBoxes.create(scope, grads, image, boxes, boxInd, options);
  }

  /**
   * Adds an {@link SetSize} operation to the graph
   *
   * @param setIndices 2D `Tensor`, indices of a `SparseTensor`.
   * @param setValues 1D `Tensor`, values of a `SparseTensor`.
   * @param setShape 1D `Tensor`, shape of a `SparseTensor`.
   * @param options carries optional attributes values
   * @return a new instance of SetSize
   * @see {@link org.tensorflow.op.core.SetSize}
   */
  public  SetSize setSize(Operand setIndices, Operand setValues, Operand setShape,
      SetSize.Options... options) {
    return SetSize.create(scope, setIndices, setValues, setShape, options);
  }

  /**
   * Adds an {@link BitwiseXor} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of BitwiseXor
   * @see {@link org.tensorflow.op.core.BitwiseXor}
   */
  public  BitwiseXor bitwiseXor(Operand x, Operand y) {
    return BitwiseXor.create(scope, x, y);
  }

  /**
   * Adds an {@link DecodeBmp} operation to the graph
   *
   * @param contents 0-D.  The BMP-encoded image.
   * @param options carries optional attributes values
   * @return a new instance of DecodeBmp
   * @see {@link org.tensorflow.op.core.DecodeBmp}
   */
  public DecodeBmp decodeBmp(Operand contents, DecodeBmp.Options... options) {
    return DecodeBmp.create(scope, contents, options);
  }

  /**
   * Adds an {@link Igammac} operation to the graph
   *
   * @param a 
   * @param x 
   * @return a new instance of Igammac
   * @see {@link org.tensorflow.op.core.Igammac}
   */
  public  Igammac igammac(Operand a, Operand x) {
    return Igammac.create(scope, a, x);
  }

  /**
   * Adds an {@link Cholesky} operation to the graph
   *
   * @param input Shape is `[..., M, M]`.
   * @return a new instance of Cholesky
   * @see {@link org.tensorflow.op.core.Cholesky}
   */
  public  Cholesky cholesky(Operand input) {
    return Cholesky.create(scope, input);
  }

  /**
   * Adds an {@link DynamicPartition} operation to the graph
   *
   * @param data 
   * @param partitions Any shape.  Indices in the range `[0, num_partitions)`.
   * @param numPartitions The number of partitions to output.
   * @return a new instance of DynamicPartition
   * @see {@link org.tensorflow.op.core.DynamicPartition}
   */
  public  DynamicPartition dynamicPartition(Operand data, Operand partitions,
      Long numPartitions) {
    return DynamicPartition.create(scope, data, partitions, numPartitions);
  }

  /**
   * Adds an {@link DecodeJpeg} operation to the graph
   *
   * @param contents 0-D.  The JPEG-encoded image.
   * @param options carries optional attributes values
   * @return a new instance of DecodeJpeg
   * @see {@link org.tensorflow.op.core.DecodeJpeg}
   */
  public DecodeJpeg decodeJpeg(Operand contents, DecodeJpeg.Options... options) {
    return DecodeJpeg.create(scope, contents, options);
  }

  /**
   * Adds an {@link ScalarSummary} operation to the graph
   *
   * @param tags Tags for the summary.
   * @param values Same shape as `tags.  Values for the summary.
   * @return a new instance of ScalarSummary
   * @see {@link org.tensorflow.op.core.ScalarSummary}
   */
  public  ScalarSummary scalarSummary(Operand tags, Operand values) {
    return ScalarSummary.create(scope, tags, values);
  }

  /**
   * Adds an {@link ResourceSparseApplyAdagradDA} operation to the graph
   *
   * @param var Should be from a Variable().
   * @param gradientAccumulator Should be from a Variable().
   * @param gradientSquaredAccumulator Should be from a Variable().
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var and accum.
   * @param lr Learning rate. Must be a scalar.
   * @param l1 L1 regularization. Must be a scalar.
   * @param l2 L2 regularization. Must be a scalar.
   * @param globalStep Training step number. Must be a scalar.
   * @param options carries optional attributes values
   * @return a new instance of ResourceSparseApplyAdagradDA
   * @see {@link org.tensorflow.op.core.ResourceSparseApplyAdagradDA}
   */
  public  ResourceSparseApplyAdagradDA resourceSparseApplyAdagradDA(Operand var,
      Operand gradientAccumulator, Operand gradientSquaredAccumulator, Operand grad,
      Operand indices, Operand lr, Operand l1, Operand l2, Operand globalStep,
      ResourceSparseApplyAdagradDA.Options... options) {
    return ResourceSparseApplyAdagradDA.create(scope, var, gradientAccumulator, gradientSquaredAccumulator, grad, indices, lr, l1, l2, globalStep, options);
  }

  /**
   * Adds an {@link NextIteration} operation to the graph
   *
   * @param data The tensor to be made available to the next iteration.
   * @return a new instance of NextIteration
   * @see {@link org.tensorflow.op.core.NextIteration}
   */
  public  NextIteration nextIteration(Operand data) {
    return NextIteration.create(scope, data);
  }

  /**
   * Adds an {@link TensorArrayScatter} operation to the graph
   *
   * @param handle The handle to a TensorArray.
   * @param indices The locations at which to write the tensor elements.
   * @param value The concatenated tensor to write to the TensorArray.
   * @param flowIn A float scalar that enforces proper chaining of operations.
   * @return a new instance of TensorArrayScatter
   * @see {@link org.tensorflow.op.core.TensorArrayScatter}
   */
  public  TensorArrayScatter tensorArrayScatter(Operand handle, Operand indices,
      Operand value, Operand flowIn) {
    return TensorArrayScatter.create(scope, handle, indices, value, flowIn);
  }

  /**
   * Adds an {@link BatchFFT3D} operation to the graph
   *
   * @param input 
   * @return a new instance of BatchFFT3D
   * @see {@link org.tensorflow.op.core.BatchFFT3D}
   */
  public BatchFFT3D batchFFT3D(Operand input) {
    return BatchFFT3D.create(scope, input);
  }

  /**
   * Adds an {@link ApproximateEqual} operation to the graph
   *
   * @param x 
   * @param y 
   * @param options carries optional attributes values
   * @return a new instance of ApproximateEqual
   * @see {@link org.tensorflow.op.core.ApproximateEqual}
   */
  public  ApproximateEqual approximateEqual(Operand x, Operand y,
      ApproximateEqual.Options... options) {
    return ApproximateEqual.create(scope, x, y, options);
  }

  /**
   * Adds an {@link ReverseSequence} operation to the graph
   *
   * @param input The input to reverse.
   * @param seqLengths 1-D with length `input.dims(batch_dim)` and
   * @param seqDim The dimension which is partially reversed.
   * @param options carries optional attributes values
   * @return a new instance of ReverseSequence
   * @see {@link org.tensorflow.op.core.ReverseSequence}
   */
  public  ReverseSequence reverseSequence(Operand input,
      Operand seqLengths, Long seqDim, ReverseSequence.Options... options) {
    return ReverseSequence.create(scope, input, seqLengths, seqDim, options);
  }

  /**
   * Adds an {@link BitwiseOr} operation to the graph
   *
   * @param x 
   * @param y 
   * @return a new instance of BitwiseOr
   * @see {@link org.tensorflow.op.core.BitwiseOr}
   */
  public  BitwiseOr bitwiseOr(Operand x, Operand y) {
    return BitwiseOr.create(scope, x, y);
  }

  /**
   * Adds an {@link OrderedMapUnstage} operation to the graph
   *
   * @param key 
   * @param indices 
   * @param dtypes 
   * @param options carries optional attributes values
   * @return a new instance of OrderedMapUnstage
   * @see {@link org.tensorflow.op.core.OrderedMapUnstage}
   */
  public OrderedMapUnstage orderedMapUnstage(Operand key, Operand indices,
      List> dtypes, OrderedMapUnstage.Options... options) {
    return OrderedMapUnstage.create(scope, key, indices, dtypes, options);
  }

  /**
   * Adds an {@link SparseSegmentSqrtNGrad} operation to the graph
   *
   * @param grad gradient propagated to the SparseSegmentSqrtN op.
   * @param indices indices passed to the corresponding SparseSegmentSqrtN op.
   * @param segmentIds segment_ids passed to the corresponding SparseSegmentSqrtN op.
   * @param outputDim0 dimension 0 of "data" passed to SparseSegmentSqrtN op.
   * @return a new instance of SparseSegmentSqrtNGrad
   * @see {@link org.tensorflow.op.core.SparseSegmentSqrtNGrad}
   */
  public  SparseSegmentSqrtNGrad sparseSegmentSqrtNGrad(Operand grad,
      Operand indices, Operand segmentIds, Operand outputDim0) {
    return SparseSegmentSqrtNGrad.create(scope, grad, indices, segmentIds, outputDim0);
  }

  /**
   * Adds an {@link ReduceMin} operation to the graph
   *
   * @param input The tensor to reduce.
   * @param axis The dimensions to reduce. Must be in the range
   * @param options carries optional attributes values
   * @return a new instance of ReduceMin
   * @see {@link org.tensorflow.op.core.ReduceMin}
   */
  public  ReduceMin reduceMin(Operand input, Operand axis,
      ReduceMin.Options... options) {
    return ReduceMin.create(scope, input, axis, options);
  }

  /**
   * Adds an {@link ScatterSub} operation to the graph
   *
   * @param ref Should be from a `Variable` node.
   * @param indices A tensor of indices into the first dimension of `ref`.
   * @param updates A tensor of updated values to subtract from `ref`.
   * @param options carries optional attributes values
   * @return a new instance of ScatterSub
   * @see {@link org.tensorflow.op.core.ScatterSub}
   */
  public  ScatterSub scatterSub(Operand ref, Operand indices,
      Operand updates, ScatterSub.Options... options) {
    return ScatterSub.create(scope, ref, indices, updates, options);
  }

  /**
   * Adds an {@link StatsAggregatorSummary} operation to the graph
   *
   * @param iterator 
   * @return a new instance of StatsAggregatorSummary
   * @see {@link org.tensorflow.op.core.StatsAggregatorSummary}
   */
  public StatsAggregatorSummary statsAggregatorSummary(Operand iterator) {
    return StatsAggregatorSummary.create(scope, iterator);
  }

  /**
   * Adds an {@link Constant} operation to the graph
   *
   * @param type the tensor datatype.
   * @param shape the tensor shape.
   * @param data a buffer containing the tensor data.
   * @throws IllegalArgumentException If the tensor datatype or shape is not compatible with the
   * @see {@link org.tensorflow.op.core.Constant}
   */
  public  Constant constant(Class type, long[] shape, ByteBuffer data) {
    return Constant.create(scope, type, shape, data);
  }

  /**
   * Returns an API that adds operations to the graph with the provided name prefix.
   *
   * @see {@link Scope#withSubScope(String)}
   */
  public Ops withSubScope(String childScopeName) {
    return new Ops(scope.withSubScope(childScopeName));
  }

  /**
   * Returns an API that uses the provided name for an op.
   *
   * @see {@link Scope#withName(String)}
   */
  public Ops withName(String opName) {
    return new Ops(scope.withName(opName));
  }

  /**
   * Returns the current {@link Scope scope} of this API
   */
  public final Scope scope() {
    return scope;
  }

  /**
   * Creates an API for adding operations to the provided {@code graph}
   */
  public static Ops create(Graph graph) {
    return new Ops(new Scope(graph));
  }
}




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