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// Generated by the protocol buffer compiler.  DO NOT EDIT!
// source: tensorflow/core/protobuf/rewriter_config.proto

package org.tensorflow.framework;

/**
 * 
 * Graph rewriting is experimental and subject to change, not covered by any
 * API stability guarantees.
 * 
* * Protobuf type {@code tensorflow.RewriterConfig} */ public final class RewriterConfig extends org.nd4j.shade.protobuf.GeneratedMessageV3 implements // @@protoc_insertion_point(message_implements:tensorflow.RewriterConfig) RewriterConfigOrBuilder { private static final long serialVersionUID = 0L; // Use RewriterConfig.newBuilder() to construct. private RewriterConfig(org.nd4j.shade.protobuf.GeneratedMessageV3.Builder builder) { super(builder); } private RewriterConfig() { layoutOptimizer_ = 0; constantFolding_ = 0; shapeOptimization_ = 0; remapping_ = 0; arithmeticOptimization_ = 0; dependencyOptimization_ = 0; loopOptimization_ = 0; functionOptimization_ = 0; debugStripper_ = 0; disableModelPruning_ = false; scopedAllocatorOptimization_ = 0; metaOptimizerIterations_ = 0; memoryOptimization_ = 0; memoryOptimizerTargetNodeNameScope_ = ""; optimizers_ = org.nd4j.shade.protobuf.LazyStringArrayList.EMPTY; customOptimizers_ = java.util.Collections.emptyList(); } @java.lang.Override public final org.nd4j.shade.protobuf.UnknownFieldSet getUnknownFields() { return this.unknownFields; } private RewriterConfig( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { this(); if (extensionRegistry == null) { throw new java.lang.NullPointerException(); } int mutable_bitField0_ = 0; org.nd4j.shade.protobuf.UnknownFieldSet.Builder unknownFields = org.nd4j.shade.protobuf.UnknownFieldSet.newBuilder(); try { boolean done = false; while (!done) { int tag = input.readTag(); switch (tag) { case 0: done = true; break; default: { if (!parseUnknownFieldProto3( input, unknownFields, extensionRegistry, tag)) { done = true; } break; } case 8: { int rawValue = input.readEnum(); layoutOptimizer_ = rawValue; break; } case 16: { disableModelPruning_ = input.readBool(); break; } case 24: { int rawValue = input.readEnum(); constantFolding_ = rawValue; break; } case 32: { int rawValue = input.readEnum(); memoryOptimization_ = rawValue; break; } case 42: { org.tensorflow.framework.AutoParallelOptions.Builder subBuilder = null; if (autoParallel_ != null) { subBuilder = autoParallel_.toBuilder(); } autoParallel_ = input.readMessage(org.tensorflow.framework.AutoParallelOptions.parser(), extensionRegistry); if (subBuilder != null) { subBuilder.mergeFrom(autoParallel_); autoParallel_ = subBuilder.buildPartial(); } break; } case 50: { java.lang.String s = input.readStringRequireUtf8(); memoryOptimizerTargetNodeNameScope_ = s; break; } case 56: { int rawValue = input.readEnum(); arithmeticOptimization_ = rawValue; break; } case 64: { int rawValue = input.readEnum(); dependencyOptimization_ = rawValue; break; } case 72: { int rawValue = input.readEnum(); loopOptimization_ = rawValue; break; } case 80: { int rawValue = input.readEnum(); functionOptimization_ = rawValue; break; } case 88: { int rawValue = input.readEnum(); debugStripper_ = rawValue; break; } case 96: { int rawValue = input.readEnum(); metaOptimizerIterations_ = rawValue; break; } case 104: { int rawValue = input.readEnum(); shapeOptimization_ = rawValue; break; } case 112: { int rawValue = input.readEnum(); remapping_ = rawValue; break; } case 120: { int rawValue = input.readEnum(); scopedAllocatorOptimization_ = rawValue; break; } case 130: { org.tensorflow.framework.ScopedAllocatorOptions.Builder subBuilder = null; if (scopedAllocatorOpts_ != null) { subBuilder = scopedAllocatorOpts_.toBuilder(); } scopedAllocatorOpts_ = input.readMessage(org.tensorflow.framework.ScopedAllocatorOptions.parser(), extensionRegistry); if (subBuilder != null) { subBuilder.mergeFrom(scopedAllocatorOpts_); scopedAllocatorOpts_ = subBuilder.buildPartial(); } break; } case 802: { java.lang.String s = input.readStringRequireUtf8(); if (!((mutable_bitField0_ & 0x00010000) == 0x00010000)) { optimizers_ = new org.nd4j.shade.protobuf.LazyStringArrayList(); mutable_bitField0_ |= 0x00010000; } optimizers_.add(s); break; } case 1602: { if (!((mutable_bitField0_ & 0x00020000) == 0x00020000)) { customOptimizers_ = new java.util.ArrayList(); mutable_bitField0_ |= 0x00020000; } customOptimizers_.add( input.readMessage(org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.parser(), extensionRegistry)); break; } } } } catch (org.nd4j.shade.protobuf.InvalidProtocolBufferException e) { throw e.setUnfinishedMessage(this); } catch (java.io.IOException e) { throw new org.nd4j.shade.protobuf.InvalidProtocolBufferException( e).setUnfinishedMessage(this); } finally { if (((mutable_bitField0_ & 0x00010000) == 0x00010000)) { optimizers_ = optimizers_.getUnmodifiableView(); } if (((mutable_bitField0_ & 0x00020000) == 0x00020000)) { customOptimizers_ = java.util.Collections.unmodifiableList(customOptimizers_); } this.unknownFields = unknownFields.build(); makeExtensionsImmutable(); } } public static final org.nd4j.shade.protobuf.Descriptors.Descriptor getDescriptor() { return org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_descriptor; } protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { return org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_fieldAccessorTable .ensureFieldAccessorsInitialized( org.tensorflow.framework.RewriterConfig.class, org.tensorflow.framework.RewriterConfig.Builder.class); } /** * Protobuf enum {@code tensorflow.RewriterConfig.Toggle} */ public enum Toggle implements org.nd4j.shade.protobuf.ProtocolMessageEnum { /** * DEFAULT = 0; */ DEFAULT(0), /** * ON = 1; */ ON(1), /** * OFF = 2; */ OFF(2), /** *
     * Enable some aggressive optimizations that use assumptions that TF graphs
     * may break. For example, assume the shape of a placeholder matches its
     * actual feed.
     * 
* * AGGRESSIVE = 3; */ AGGRESSIVE(3), UNRECOGNIZED(-1), ; /** * DEFAULT = 0; */ public static final int DEFAULT_VALUE = 0; /** * ON = 1; */ public static final int ON_VALUE = 1; /** * OFF = 2; */ public static final int OFF_VALUE = 2; /** *
     * Enable some aggressive optimizations that use assumptions that TF graphs
     * may break. For example, assume the shape of a placeholder matches its
     * actual feed.
     * 
* * AGGRESSIVE = 3; */ public static final int AGGRESSIVE_VALUE = 3; public final int getNumber() { if (this == UNRECOGNIZED) { throw new java.lang.IllegalArgumentException( "Can't get the number of an unknown enum value."); } return value; } /** * @deprecated Use {@link #forNumber(int)} instead. */ @java.lang.Deprecated public static Toggle valueOf(int value) { return forNumber(value); } public static Toggle forNumber(int value) { switch (value) { case 0: return DEFAULT; case 1: return ON; case 2: return OFF; case 3: return AGGRESSIVE; default: return null; } } public static org.nd4j.shade.protobuf.Internal.EnumLiteMap internalGetValueMap() { return internalValueMap; } private static final org.nd4j.shade.protobuf.Internal.EnumLiteMap< Toggle> internalValueMap = new org.nd4j.shade.protobuf.Internal.EnumLiteMap() { public Toggle findValueByNumber(int number) { return Toggle.forNumber(number); } }; public final org.nd4j.shade.protobuf.Descriptors.EnumValueDescriptor getValueDescriptor() { return getDescriptor().getValues().get(ordinal()); } public final org.nd4j.shade.protobuf.Descriptors.EnumDescriptor getDescriptorForType() { return getDescriptor(); } public static final org.nd4j.shade.protobuf.Descriptors.EnumDescriptor getDescriptor() { return org.tensorflow.framework.RewriterConfig.getDescriptor().getEnumTypes().get(0); } private static final Toggle[] VALUES = values(); public static Toggle valueOf( org.nd4j.shade.protobuf.Descriptors.EnumValueDescriptor desc) { if (desc.getType() != getDescriptor()) { throw new java.lang.IllegalArgumentException( "EnumValueDescriptor is not for this type."); } if (desc.getIndex() == -1) { return UNRECOGNIZED; } return VALUES[desc.getIndex()]; } private final int value; private Toggle(int value) { this.value = value; } // @@protoc_insertion_point(enum_scope:tensorflow.RewriterConfig.Toggle) } /** *
   * Enum controlling the number of times to run optimizers. The default is to
   * run them once.
   * 
* * Protobuf enum {@code tensorflow.RewriterConfig.NumIterationsType} */ public enum NumIterationsType implements org.nd4j.shade.protobuf.ProtocolMessageEnum { /** * DEFAULT_NUM_ITERS = 0; */ DEFAULT_NUM_ITERS(0), /** * ONE = 1; */ ONE(1), /** * TWO = 2; */ TWO(2), UNRECOGNIZED(-1), ; /** * DEFAULT_NUM_ITERS = 0; */ public static final int DEFAULT_NUM_ITERS_VALUE = 0; /** * ONE = 1; */ public static final int ONE_VALUE = 1; /** * TWO = 2; */ public static final int TWO_VALUE = 2; public final int getNumber() { if (this == UNRECOGNIZED) { throw new java.lang.IllegalArgumentException( "Can't get the number of an unknown enum value."); } return value; } /** * @deprecated Use {@link #forNumber(int)} instead. */ @java.lang.Deprecated public static NumIterationsType valueOf(int value) { return forNumber(value); } public static NumIterationsType forNumber(int value) { switch (value) { case 0: return DEFAULT_NUM_ITERS; case 1: return ONE; case 2: return TWO; default: return null; } } public static org.nd4j.shade.protobuf.Internal.EnumLiteMap internalGetValueMap() { return internalValueMap; } private static final org.nd4j.shade.protobuf.Internal.EnumLiteMap< NumIterationsType> internalValueMap = new org.nd4j.shade.protobuf.Internal.EnumLiteMap() { public NumIterationsType findValueByNumber(int number) { return NumIterationsType.forNumber(number); } }; public final org.nd4j.shade.protobuf.Descriptors.EnumValueDescriptor getValueDescriptor() { return getDescriptor().getValues().get(ordinal()); } public final org.nd4j.shade.protobuf.Descriptors.EnumDescriptor getDescriptorForType() { return getDescriptor(); } public static final org.nd4j.shade.protobuf.Descriptors.EnumDescriptor getDescriptor() { return org.tensorflow.framework.RewriterConfig.getDescriptor().getEnumTypes().get(1); } private static final NumIterationsType[] VALUES = values(); public static NumIterationsType valueOf( org.nd4j.shade.protobuf.Descriptors.EnumValueDescriptor desc) { if (desc.getType() != getDescriptor()) { throw new java.lang.IllegalArgumentException( "EnumValueDescriptor is not for this type."); } if (desc.getIndex() == -1) { return UNRECOGNIZED; } return VALUES[desc.getIndex()]; } private final int value; private NumIterationsType(int value) { this.value = value; } // @@protoc_insertion_point(enum_scope:tensorflow.RewriterConfig.NumIterationsType) } /** * Protobuf enum {@code tensorflow.RewriterConfig.MemOptType} */ public enum MemOptType implements org.nd4j.shade.protobuf.ProtocolMessageEnum { /** *
     * The default setting (SCHEDULING and SWAPPING HEURISTICS only)
     * 
* * DEFAULT_MEM_OPT = 0; */ DEFAULT_MEM_OPT(0), /** *
     * Disabled in the meta-optimizer.
     * 
* * NO_MEM_OPT = 1; */ NO_MEM_OPT(1), /** *
     * Driven by manual op-level annotations.
     * 
* * MANUAL = 2; */ MANUAL(2), /** *
     * Swapping heuristic will move a tensor from the GPU to the CPU and move
     * it back when needed to reduce peak memory usage.
     * 
* * SWAPPING_HEURISTICS = 4; */ SWAPPING_HEURISTICS(4), /** *
     * Recomputation heuristics will recompute ops (such as Relu activation)
     * during backprop instead of storing them, reducing peak memory usage.
     * 
* * RECOMPUTATION_HEURISTICS = 5; */ RECOMPUTATION_HEURISTICS(5), /** *
     * Scheduling will split big ops such as AddN and try to enforce a schedule
     * of the new computations that decreases peak memory usage.
     * 
* * SCHEDULING_HEURISTICS = 6; */ SCHEDULING_HEURISTICS(6), /** *
     * Use any combination of swapping and recomputation heuristics.
     * 
* * HEURISTICS = 3; */ HEURISTICS(3), UNRECOGNIZED(-1), ; /** *
     * The default setting (SCHEDULING and SWAPPING HEURISTICS only)
     * 
* * DEFAULT_MEM_OPT = 0; */ public static final int DEFAULT_MEM_OPT_VALUE = 0; /** *
     * Disabled in the meta-optimizer.
     * 
* * NO_MEM_OPT = 1; */ public static final int NO_MEM_OPT_VALUE = 1; /** *
     * Driven by manual op-level annotations.
     * 
* * MANUAL = 2; */ public static final int MANUAL_VALUE = 2; /** *
     * Swapping heuristic will move a tensor from the GPU to the CPU and move
     * it back when needed to reduce peak memory usage.
     * 
* * SWAPPING_HEURISTICS = 4; */ public static final int SWAPPING_HEURISTICS_VALUE = 4; /** *
     * Recomputation heuristics will recompute ops (such as Relu activation)
     * during backprop instead of storing them, reducing peak memory usage.
     * 
* * RECOMPUTATION_HEURISTICS = 5; */ public static final int RECOMPUTATION_HEURISTICS_VALUE = 5; /** *
     * Scheduling will split big ops such as AddN and try to enforce a schedule
     * of the new computations that decreases peak memory usage.
     * 
* * SCHEDULING_HEURISTICS = 6; */ public static final int SCHEDULING_HEURISTICS_VALUE = 6; /** *
     * Use any combination of swapping and recomputation heuristics.
     * 
* * HEURISTICS = 3; */ public static final int HEURISTICS_VALUE = 3; public final int getNumber() { if (this == UNRECOGNIZED) { throw new java.lang.IllegalArgumentException( "Can't get the number of an unknown enum value."); } return value; } /** * @deprecated Use {@link #forNumber(int)} instead. */ @java.lang.Deprecated public static MemOptType valueOf(int value) { return forNumber(value); } public static MemOptType forNumber(int value) { switch (value) { case 0: return DEFAULT_MEM_OPT; case 1: return NO_MEM_OPT; case 2: return MANUAL; case 4: return SWAPPING_HEURISTICS; case 5: return RECOMPUTATION_HEURISTICS; case 6: return SCHEDULING_HEURISTICS; case 3: return HEURISTICS; default: return null; } } public static org.nd4j.shade.protobuf.Internal.EnumLiteMap internalGetValueMap() { return internalValueMap; } private static final org.nd4j.shade.protobuf.Internal.EnumLiteMap< MemOptType> internalValueMap = new org.nd4j.shade.protobuf.Internal.EnumLiteMap() { public MemOptType findValueByNumber(int number) { return MemOptType.forNumber(number); } }; public final org.nd4j.shade.protobuf.Descriptors.EnumValueDescriptor getValueDescriptor() { return getDescriptor().getValues().get(ordinal()); } public final org.nd4j.shade.protobuf.Descriptors.EnumDescriptor getDescriptorForType() { return getDescriptor(); } public static final org.nd4j.shade.protobuf.Descriptors.EnumDescriptor getDescriptor() { return org.tensorflow.framework.RewriterConfig.getDescriptor().getEnumTypes().get(2); } private static final MemOptType[] VALUES = values(); public static MemOptType valueOf( org.nd4j.shade.protobuf.Descriptors.EnumValueDescriptor desc) { if (desc.getType() != getDescriptor()) { throw new java.lang.IllegalArgumentException( "EnumValueDescriptor is not for this type."); } if (desc.getIndex() == -1) { return UNRECOGNIZED; } return VALUES[desc.getIndex()]; } private final int value; private MemOptType(int value) { this.value = value; } // @@protoc_insertion_point(enum_scope:tensorflow.RewriterConfig.MemOptType) } public interface CustomGraphOptimizerOrBuilder extends // @@protoc_insertion_point(interface_extends:tensorflow.RewriterConfig.CustomGraphOptimizer) org.nd4j.shade.protobuf.MessageOrBuilder { /** * string name = 1; */ java.lang.String getName(); /** * string name = 1; */ org.nd4j.shade.protobuf.ByteString getNameBytes(); /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ int getParameterMapCount(); /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ boolean containsParameterMap( java.lang.String key); /** * Use {@link #getParameterMapMap()} instead. */ @java.lang.Deprecated java.util.Map getParameterMap(); /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ java.util.Map getParameterMapMap(); /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ org.tensorflow.framework.AttrValue getParameterMapOrDefault( java.lang.String key, org.tensorflow.framework.AttrValue defaultValue); /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ org.tensorflow.framework.AttrValue getParameterMapOrThrow( java.lang.String key); } /** *
   * Message to describe custom graph optimizer and its parameters
   * 
* * Protobuf type {@code tensorflow.RewriterConfig.CustomGraphOptimizer} */ public static final class CustomGraphOptimizer extends org.nd4j.shade.protobuf.GeneratedMessageV3 implements // @@protoc_insertion_point(message_implements:tensorflow.RewriterConfig.CustomGraphOptimizer) CustomGraphOptimizerOrBuilder { private static final long serialVersionUID = 0L; // Use CustomGraphOptimizer.newBuilder() to construct. private CustomGraphOptimizer(org.nd4j.shade.protobuf.GeneratedMessageV3.Builder builder) { super(builder); } private CustomGraphOptimizer() { name_ = ""; } @java.lang.Override public final org.nd4j.shade.protobuf.UnknownFieldSet getUnknownFields() { return this.unknownFields; } private CustomGraphOptimizer( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { this(); if (extensionRegistry == null) { throw new java.lang.NullPointerException(); } int mutable_bitField0_ = 0; org.nd4j.shade.protobuf.UnknownFieldSet.Builder unknownFields = org.nd4j.shade.protobuf.UnknownFieldSet.newBuilder(); try { boolean done = false; while (!done) { int tag = input.readTag(); switch (tag) { case 0: done = true; break; default: { if (!parseUnknownFieldProto3( input, unknownFields, extensionRegistry, tag)) { done = true; } break; } case 10: { java.lang.String s = input.readStringRequireUtf8(); name_ = s; break; } case 18: { if (!((mutable_bitField0_ & 0x00000002) == 0x00000002)) { parameterMap_ = org.nd4j.shade.protobuf.MapField.newMapField( ParameterMapDefaultEntryHolder.defaultEntry); mutable_bitField0_ |= 0x00000002; } org.nd4j.shade.protobuf.MapEntry parameterMap__ = input.readMessage( ParameterMapDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry); parameterMap_.getMutableMap().put( parameterMap__.getKey(), parameterMap__.getValue()); break; } } } } catch (org.nd4j.shade.protobuf.InvalidProtocolBufferException e) { throw e.setUnfinishedMessage(this); } catch (java.io.IOException e) { throw new org.nd4j.shade.protobuf.InvalidProtocolBufferException( e).setUnfinishedMessage(this); } finally { this.unknownFields = unknownFields.build(); makeExtensionsImmutable(); } } public static final org.nd4j.shade.protobuf.Descriptors.Descriptor getDescriptor() { return org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_CustomGraphOptimizer_descriptor; } @SuppressWarnings({"rawtypes"}) protected org.nd4j.shade.protobuf.MapField internalGetMapField( int number) { switch (number) { case 2: return internalGetParameterMap(); default: throw new RuntimeException( "Invalid map field number: " + number); } } protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { return org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_CustomGraphOptimizer_fieldAccessorTable .ensureFieldAccessorsInitialized( org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.class, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder.class); } private int bitField0_; public static final int NAME_FIELD_NUMBER = 1; private volatile java.lang.Object name_; /** * string name = 1; */ public java.lang.String getName() { java.lang.Object ref = name_; if (ref instanceof java.lang.String) { return (java.lang.String) ref; } else { org.nd4j.shade.protobuf.ByteString bs = (org.nd4j.shade.protobuf.ByteString) ref; java.lang.String s = bs.toStringUtf8(); name_ = s; return s; } } /** * string name = 1; */ public org.nd4j.shade.protobuf.ByteString getNameBytes() { java.lang.Object ref = name_; if (ref instanceof java.lang.String) { org.nd4j.shade.protobuf.ByteString b = org.nd4j.shade.protobuf.ByteString.copyFromUtf8( (java.lang.String) ref); name_ = b; return b; } else { return (org.nd4j.shade.protobuf.ByteString) ref; } } public static final int PARAMETER_MAP_FIELD_NUMBER = 2; private static final class ParameterMapDefaultEntryHolder { static final org.nd4j.shade.protobuf.MapEntry< java.lang.String, org.tensorflow.framework.AttrValue> defaultEntry = org.nd4j.shade.protobuf.MapEntry .newDefaultInstance( org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_CustomGraphOptimizer_ParameterMapEntry_descriptor, org.nd4j.shade.protobuf.WireFormat.FieldType.STRING, "", org.nd4j.shade.protobuf.WireFormat.FieldType.MESSAGE, org.tensorflow.framework.AttrValue.getDefaultInstance()); } private org.nd4j.shade.protobuf.MapField< java.lang.String, org.tensorflow.framework.AttrValue> parameterMap_; private org.nd4j.shade.protobuf.MapField internalGetParameterMap() { if (parameterMap_ == null) { return org.nd4j.shade.protobuf.MapField.emptyMapField( ParameterMapDefaultEntryHolder.defaultEntry); } return parameterMap_; } public int getParameterMapCount() { return internalGetParameterMap().getMap().size(); } /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ public boolean containsParameterMap( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } return internalGetParameterMap().getMap().containsKey(key); } /** * Use {@link #getParameterMapMap()} instead. */ @java.lang.Deprecated public java.util.Map getParameterMap() { return getParameterMapMap(); } /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ public java.util.Map getParameterMapMap() { return internalGetParameterMap().getMap(); } /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ public org.tensorflow.framework.AttrValue getParameterMapOrDefault( java.lang.String key, org.tensorflow.framework.AttrValue defaultValue) { if (key == null) { throw new java.lang.NullPointerException(); } java.util.Map map = internalGetParameterMap().getMap(); return map.containsKey(key) ? map.get(key) : defaultValue; } /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ public org.tensorflow.framework.AttrValue getParameterMapOrThrow( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } java.util.Map map = internalGetParameterMap().getMap(); if (!map.containsKey(key)) { throw new java.lang.IllegalArgumentException(); } return map.get(key); } private byte memoizedIsInitialized = -1; public final boolean isInitialized() { byte isInitialized = memoizedIsInitialized; if (isInitialized == 1) return true; if (isInitialized == 0) return false; memoizedIsInitialized = 1; return true; } public void writeTo(org.nd4j.shade.protobuf.CodedOutputStream output) throws java.io.IOException { if (!getNameBytes().isEmpty()) { org.nd4j.shade.protobuf.GeneratedMessageV3.writeString(output, 1, name_); } org.nd4j.shade.protobuf.GeneratedMessageV3 .serializeStringMapTo( output, internalGetParameterMap(), ParameterMapDefaultEntryHolder.defaultEntry, 2); unknownFields.writeTo(output); } public int getSerializedSize() { int size = memoizedSize; if (size != -1) return size; size = 0; if (!getNameBytes().isEmpty()) { size += org.nd4j.shade.protobuf.GeneratedMessageV3.computeStringSize(1, name_); } for (java.util.Map.Entry entry : internalGetParameterMap().getMap().entrySet()) { org.nd4j.shade.protobuf.MapEntry parameterMap__ = ParameterMapDefaultEntryHolder.defaultEntry.newBuilderForType() .setKey(entry.getKey()) .setValue(entry.getValue()) .build(); size += org.nd4j.shade.protobuf.CodedOutputStream .computeMessageSize(2, parameterMap__); } size += unknownFields.getSerializedSize(); memoizedSize = size; return size; } @java.lang.Override public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } if (!(obj instanceof org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer)) { return super.equals(obj); } org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer other = (org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer) obj; boolean result = true; result = result && getName() .equals(other.getName()); result = result && internalGetParameterMap().equals( other.internalGetParameterMap()); result = result && unknownFields.equals(other.unknownFields); return result; } @java.lang.Override public int hashCode() { if (memoizedHashCode != 0) { return memoizedHashCode; } int hash = 41; hash = (19 * hash) + getDescriptor().hashCode(); hash = (37 * hash) + NAME_FIELD_NUMBER; hash = (53 * hash) + getName().hashCode(); if (!internalGetParameterMap().getMap().isEmpty()) { hash = (37 * hash) + PARAMETER_MAP_FIELD_NUMBER; hash = (53 * hash) + internalGetParameterMap().hashCode(); } hash = (29 * hash) + unknownFields.hashCode(); memoizedHashCode = hash; return hash; } public static org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer parseFrom( java.nio.ByteBuffer data) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer parseFrom( java.nio.ByteBuffer data, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer parseFrom( org.nd4j.shade.protobuf.ByteString data) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer parseFrom( org.nd4j.shade.protobuf.ByteString data, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer parseFrom(byte[] data) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer parseFrom( byte[] data, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer parseFrom(java.io.InputStream input) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } public static org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer parseFrom( java.io.InputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } public static org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } public static org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer parseDelimitedFrom( java.io.InputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } public static org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer parseFrom( org.nd4j.shade.protobuf.CodedInputStream input) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } public static org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer parseFrom( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } public Builder newBuilderForType() { return newBuilder(); } public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } public static Builder newBuilder(org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer prototype) { return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); } public Builder toBuilder() { return this == DEFAULT_INSTANCE ? new Builder() : new Builder().mergeFrom(this); } @java.lang.Override protected Builder newBuilderForType( org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) { Builder builder = new Builder(parent); return builder; } /** *
     * Message to describe custom graph optimizer and its parameters
     * 
* * Protobuf type {@code tensorflow.RewriterConfig.CustomGraphOptimizer} */ public static final class Builder extends org.nd4j.shade.protobuf.GeneratedMessageV3.Builder implements // @@protoc_insertion_point(builder_implements:tensorflow.RewriterConfig.CustomGraphOptimizer) org.tensorflow.framework.RewriterConfig.CustomGraphOptimizerOrBuilder { public static final org.nd4j.shade.protobuf.Descriptors.Descriptor getDescriptor() { return org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_CustomGraphOptimizer_descriptor; } @SuppressWarnings({"rawtypes"}) protected org.nd4j.shade.protobuf.MapField internalGetMapField( int number) { switch (number) { case 2: return internalGetParameterMap(); default: throw new RuntimeException( "Invalid map field number: " + number); } } @SuppressWarnings({"rawtypes"}) protected org.nd4j.shade.protobuf.MapField internalGetMutableMapField( int number) { switch (number) { case 2: return internalGetMutableParameterMap(); default: throw new RuntimeException( "Invalid map field number: " + number); } } protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { return org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_CustomGraphOptimizer_fieldAccessorTable .ensureFieldAccessorsInitialized( org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.class, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder.class); } // Construct using org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.newBuilder() private Builder() { maybeForceBuilderInitialization(); } private Builder( org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) { super(parent); maybeForceBuilderInitialization(); } private void maybeForceBuilderInitialization() { if (org.nd4j.shade.protobuf.GeneratedMessageV3 .alwaysUseFieldBuilders) { } } public Builder clear() { super.clear(); name_ = ""; internalGetMutableParameterMap().clear(); return this; } public org.nd4j.shade.protobuf.Descriptors.Descriptor getDescriptorForType() { return org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_CustomGraphOptimizer_descriptor; } public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer getDefaultInstanceForType() { return org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.getDefaultInstance(); } public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer build() { org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer buildPartial() { org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer result = new org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer(this); int from_bitField0_ = bitField0_; int to_bitField0_ = 0; result.name_ = name_; result.parameterMap_ = internalGetParameterMap(); result.parameterMap_.makeImmutable(); result.bitField0_ = to_bitField0_; onBuilt(); return result; } public Builder clone() { return (Builder) super.clone(); } public Builder setField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) { return (Builder) super.setField(field, value); } public Builder clearField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field) { return (Builder) super.clearField(field); } public Builder clearOneof( org.nd4j.shade.protobuf.Descriptors.OneofDescriptor oneof) { return (Builder) super.clearOneof(oneof); } public Builder setRepeatedField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value) { return (Builder) super.setRepeatedField(field, index, value); } public Builder addRepeatedField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) { return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(org.nd4j.shade.protobuf.Message other) { if (other instanceof org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer) { return mergeFrom((org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer)other); } else { super.mergeFrom(other); return this; } } public Builder mergeFrom(org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer other) { if (other == org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.getDefaultInstance()) return this; if (!other.getName().isEmpty()) { name_ = other.name_; onChanged(); } internalGetMutableParameterMap().mergeFrom( other.internalGetParameterMap()); this.mergeUnknownFields(other.unknownFields); onChanged(); return this; } public final boolean isInitialized() { return true; } public Builder mergeFrom( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (org.nd4j.shade.protobuf.InvalidProtocolBufferException e) { parsedMessage = (org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer) e.getUnfinishedMessage(); throw e.unwrapIOException(); } finally { if (parsedMessage != null) { mergeFrom(parsedMessage); } } return this; } private int bitField0_; private java.lang.Object name_ = ""; /** * string name = 1; */ public java.lang.String getName() { java.lang.Object ref = name_; if (!(ref instanceof java.lang.String)) { org.nd4j.shade.protobuf.ByteString bs = (org.nd4j.shade.protobuf.ByteString) ref; java.lang.String s = bs.toStringUtf8(); name_ = s; return s; } else { return (java.lang.String) ref; } } /** * string name = 1; */ public org.nd4j.shade.protobuf.ByteString getNameBytes() { java.lang.Object ref = name_; if (ref instanceof String) { org.nd4j.shade.protobuf.ByteString b = org.nd4j.shade.protobuf.ByteString.copyFromUtf8( (java.lang.String) ref); name_ = b; return b; } else { return (org.nd4j.shade.protobuf.ByteString) ref; } } /** * string name = 1; */ public Builder setName( java.lang.String value) { if (value == null) { throw new NullPointerException(); } name_ = value; onChanged(); return this; } /** * string name = 1; */ public Builder clearName() { name_ = getDefaultInstance().getName(); onChanged(); return this; } /** * string name = 1; */ public Builder setNameBytes( org.nd4j.shade.protobuf.ByteString value) { if (value == null) { throw new NullPointerException(); } checkByteStringIsUtf8(value); name_ = value; onChanged(); return this; } private org.nd4j.shade.protobuf.MapField< java.lang.String, org.tensorflow.framework.AttrValue> parameterMap_; private org.nd4j.shade.protobuf.MapField internalGetParameterMap() { if (parameterMap_ == null) { return org.nd4j.shade.protobuf.MapField.emptyMapField( ParameterMapDefaultEntryHolder.defaultEntry); } return parameterMap_; } private org.nd4j.shade.protobuf.MapField internalGetMutableParameterMap() { onChanged();; if (parameterMap_ == null) { parameterMap_ = org.nd4j.shade.protobuf.MapField.newMapField( ParameterMapDefaultEntryHolder.defaultEntry); } if (!parameterMap_.isMutable()) { parameterMap_ = parameterMap_.copy(); } return parameterMap_; } public int getParameterMapCount() { return internalGetParameterMap().getMap().size(); } /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ public boolean containsParameterMap( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } return internalGetParameterMap().getMap().containsKey(key); } /** * Use {@link #getParameterMapMap()} instead. */ @java.lang.Deprecated public java.util.Map getParameterMap() { return getParameterMapMap(); } /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ public java.util.Map getParameterMapMap() { return internalGetParameterMap().getMap(); } /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ public org.tensorflow.framework.AttrValue getParameterMapOrDefault( java.lang.String key, org.tensorflow.framework.AttrValue defaultValue) { if (key == null) { throw new java.lang.NullPointerException(); } java.util.Map map = internalGetParameterMap().getMap(); return map.containsKey(key) ? map.get(key) : defaultValue; } /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ public org.tensorflow.framework.AttrValue getParameterMapOrThrow( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } java.util.Map map = internalGetParameterMap().getMap(); if (!map.containsKey(key)) { throw new java.lang.IllegalArgumentException(); } return map.get(key); } public Builder clearParameterMap() { internalGetMutableParameterMap().getMutableMap() .clear(); return this; } /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ public Builder removeParameterMap( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } internalGetMutableParameterMap().getMutableMap() .remove(key); return this; } /** * Use alternate mutation accessors instead. */ @java.lang.Deprecated public java.util.Map getMutableParameterMap() { return internalGetMutableParameterMap().getMutableMap(); } /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ public Builder putParameterMap( java.lang.String key, org.tensorflow.framework.AttrValue value) { if (key == null) { throw new java.lang.NullPointerException(); } if (value == null) { throw new java.lang.NullPointerException(); } internalGetMutableParameterMap().getMutableMap() .put(key, value); return this; } /** * map<string, .tensorflow.AttrValue> parameter_map = 2; */ public Builder putAllParameterMap( java.util.Map values) { internalGetMutableParameterMap().getMutableMap() .putAll(values); return this; } public final Builder setUnknownFields( final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) { return super.setUnknownFieldsProto3(unknownFields); } public final Builder mergeUnknownFields( final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) { return super.mergeUnknownFields(unknownFields); } // @@protoc_insertion_point(builder_scope:tensorflow.RewriterConfig.CustomGraphOptimizer) } // @@protoc_insertion_point(class_scope:tensorflow.RewriterConfig.CustomGraphOptimizer) private static final org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer DEFAULT_INSTANCE; static { DEFAULT_INSTANCE = new org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer(); } public static org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer getDefaultInstance() { return DEFAULT_INSTANCE; } private static final org.nd4j.shade.protobuf.Parser PARSER = new org.nd4j.shade.protobuf.AbstractParser() { public CustomGraphOptimizer parsePartialFrom( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return new CustomGraphOptimizer(input, extensionRegistry); } }; public static org.nd4j.shade.protobuf.Parser parser() { return PARSER; } @java.lang.Override public org.nd4j.shade.protobuf.Parser getParserForType() { return PARSER; } public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer getDefaultInstanceForType() { return DEFAULT_INSTANCE; } } private int bitField0_; public static final int LAYOUT_OPTIMIZER_FIELD_NUMBER = 1; private int layoutOptimizer_; /** *
   * Optimize tensor layouts (default is ON)
   * e.g. This will try to use NCHW layout on GPU which is faster.
   * 
* * .tensorflow.RewriterConfig.Toggle layout_optimizer = 1; */ public int getLayoutOptimizerValue() { return layoutOptimizer_; } /** *
   * Optimize tensor layouts (default is ON)
   * e.g. This will try to use NCHW layout on GPU which is faster.
   * 
* * .tensorflow.RewriterConfig.Toggle layout_optimizer = 1; */ public org.tensorflow.framework.RewriterConfig.Toggle getLayoutOptimizer() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(layoutOptimizer_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } public static final int CONSTANT_FOLDING_FIELD_NUMBER = 3; private int constantFolding_; /** *
   * Fold constants (default is ON)
   * Statically infer the value of tensors when possible, and materialize the
   * result using constants.
   * 
* * .tensorflow.RewriterConfig.Toggle constant_folding = 3; */ public int getConstantFoldingValue() { return constantFolding_; } /** *
   * Fold constants (default is ON)
   * Statically infer the value of tensors when possible, and materialize the
   * result using constants.
   * 
* * .tensorflow.RewriterConfig.Toggle constant_folding = 3; */ public org.tensorflow.framework.RewriterConfig.Toggle getConstantFolding() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(constantFolding_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } public static final int SHAPE_OPTIMIZATION_FIELD_NUMBER = 13; private int shapeOptimization_; /** *
   * Shape optimizations (default is ON)
   * Simplify computations made on shapes.
   * 
* * .tensorflow.RewriterConfig.Toggle shape_optimization = 13; */ public int getShapeOptimizationValue() { return shapeOptimization_; } /** *
   * Shape optimizations (default is ON)
   * Simplify computations made on shapes.
   * 
* * .tensorflow.RewriterConfig.Toggle shape_optimization = 13; */ public org.tensorflow.framework.RewriterConfig.Toggle getShapeOptimization() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(shapeOptimization_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } public static final int REMAPPING_FIELD_NUMBER = 14; private int remapping_; /** *
   * Remapping (default is ON)
   * Remap subgraphs onto more efficient implementations.
   * 
* * .tensorflow.RewriterConfig.Toggle remapping = 14; */ public int getRemappingValue() { return remapping_; } /** *
   * Remapping (default is ON)
   * Remap subgraphs onto more efficient implementations.
   * 
* * .tensorflow.RewriterConfig.Toggle remapping = 14; */ public org.tensorflow.framework.RewriterConfig.Toggle getRemapping() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(remapping_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } public static final int ARITHMETIC_OPTIMIZATION_FIELD_NUMBER = 7; private int arithmeticOptimization_; /** *
   * Arithmetic optimizations (default is ON)
   * e.g. Simplify arithmetic ops; merge ops with same value (like constants).
   * 
* * .tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7; */ public int getArithmeticOptimizationValue() { return arithmeticOptimization_; } /** *
   * Arithmetic optimizations (default is ON)
   * e.g. Simplify arithmetic ops; merge ops with same value (like constants).
   * 
* * .tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7; */ public org.tensorflow.framework.RewriterConfig.Toggle getArithmeticOptimization() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(arithmeticOptimization_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } public static final int DEPENDENCY_OPTIMIZATION_FIELD_NUMBER = 8; private int dependencyOptimization_; /** *
   * Control dependency optimizations (default is ON).
   * Remove redundant control dependencies, which may enable other optimization.
   * 
* * .tensorflow.RewriterConfig.Toggle dependency_optimization = 8; */ public int getDependencyOptimizationValue() { return dependencyOptimization_; } /** *
   * Control dependency optimizations (default is ON).
   * Remove redundant control dependencies, which may enable other optimization.
   * 
* * .tensorflow.RewriterConfig.Toggle dependency_optimization = 8; */ public org.tensorflow.framework.RewriterConfig.Toggle getDependencyOptimization() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(dependencyOptimization_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } public static final int LOOP_OPTIMIZATION_FIELD_NUMBER = 9; private int loopOptimization_; /** *
   * Loop optimizations (default is ON).
   * 
* * .tensorflow.RewriterConfig.Toggle loop_optimization = 9; */ public int getLoopOptimizationValue() { return loopOptimization_; } /** *
   * Loop optimizations (default is ON).
   * 
* * .tensorflow.RewriterConfig.Toggle loop_optimization = 9; */ public org.tensorflow.framework.RewriterConfig.Toggle getLoopOptimization() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(loopOptimization_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } public static final int FUNCTION_OPTIMIZATION_FIELD_NUMBER = 10; private int functionOptimization_; /** *
   * Function optimizations (default is ON).
   * 
* * .tensorflow.RewriterConfig.Toggle function_optimization = 10; */ public int getFunctionOptimizationValue() { return functionOptimization_; } /** *
   * Function optimizations (default is ON).
   * 
* * .tensorflow.RewriterConfig.Toggle function_optimization = 10; */ public org.tensorflow.framework.RewriterConfig.Toggle getFunctionOptimization() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(functionOptimization_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } public static final int DEBUG_STRIPPER_FIELD_NUMBER = 11; private int debugStripper_; /** *
   * Strips debug-related nodes from the graph (off by default).
   * 
* * .tensorflow.RewriterConfig.Toggle debug_stripper = 11; */ public int getDebugStripperValue() { return debugStripper_; } /** *
   * Strips debug-related nodes from the graph (off by default).
   * 
* * .tensorflow.RewriterConfig.Toggle debug_stripper = 11; */ public org.tensorflow.framework.RewriterConfig.Toggle getDebugStripper() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(debugStripper_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } public static final int DISABLE_MODEL_PRUNING_FIELD_NUMBER = 2; private boolean disableModelPruning_; /** *
   * If true, don't remove unnecessary ops from the graph
   * 
* * bool disable_model_pruning = 2; */ public boolean getDisableModelPruning() { return disableModelPruning_; } public static final int SCOPED_ALLOCATOR_OPTIMIZATION_FIELD_NUMBER = 15; private int scopedAllocatorOptimization_; /** *
   * Try to allocate some independent Op outputs contiguously in order to
   * merge or eliminate downstream Ops (off by default).
   * 
* * .tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15; */ public int getScopedAllocatorOptimizationValue() { return scopedAllocatorOptimization_; } /** *
   * Try to allocate some independent Op outputs contiguously in order to
   * merge or eliminate downstream Ops (off by default).
   * 
* * .tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15; */ public org.tensorflow.framework.RewriterConfig.Toggle getScopedAllocatorOptimization() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(scopedAllocatorOptimization_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } public static final int META_OPTIMIZER_ITERATIONS_FIELD_NUMBER = 12; private int metaOptimizerIterations_; /** *
   * Controls how many times we run the optimizers in meta optimizer (default
   * is once).
   * 
* * .tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12; */ public int getMetaOptimizerIterationsValue() { return metaOptimizerIterations_; } /** *
   * Controls how many times we run the optimizers in meta optimizer (default
   * is once).
   * 
* * .tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12; */ public org.tensorflow.framework.RewriterConfig.NumIterationsType getMetaOptimizerIterations() { org.tensorflow.framework.RewriterConfig.NumIterationsType result = org.tensorflow.framework.RewriterConfig.NumIterationsType.valueOf(metaOptimizerIterations_); return result == null ? org.tensorflow.framework.RewriterConfig.NumIterationsType.UNRECOGNIZED : result; } public static final int MEMORY_OPTIMIZATION_FIELD_NUMBER = 4; private int memoryOptimization_; /** *
   * Configures memory optimization passes through the meta-optimizer. Has no
   * effect on manually requested memory optimization passes in the optimizers
   * field.
   * 
* * .tensorflow.RewriterConfig.MemOptType memory_optimization = 4; */ public int getMemoryOptimizationValue() { return memoryOptimization_; } /** *
   * Configures memory optimization passes through the meta-optimizer. Has no
   * effect on manually requested memory optimization passes in the optimizers
   * field.
   * 
* * .tensorflow.RewriterConfig.MemOptType memory_optimization = 4; */ public org.tensorflow.framework.RewriterConfig.MemOptType getMemoryOptimization() { org.tensorflow.framework.RewriterConfig.MemOptType result = org.tensorflow.framework.RewriterConfig.MemOptType.valueOf(memoryOptimization_); return result == null ? org.tensorflow.framework.RewriterConfig.MemOptType.UNRECOGNIZED : result; } public static final int MEMORY_OPTIMIZER_TARGET_NODE_NAME_SCOPE_FIELD_NUMBER = 6; private volatile java.lang.Object memoryOptimizerTargetNodeNameScope_; /** *
   * A node name scope for node names which are valid outputs of recompuations.
   * Inputs to nodes that match this scope may be recomputed (subject either to
   * manual annotation of those input nodes or to manual annotation and
   * heuristics depending on memory_optimization), but the nodes themselves will
   * not be recomputed. This matches any sub-scopes as well, meaning the scope
   * can appear not just as a top-level scope. For example, if the value is
   * "gradients/", the default, it will match node name "gradients/foo",
   * "foo/gradients/bar", but not "foo_gradients/"
   * 
* * string memory_optimizer_target_node_name_scope = 6; */ public java.lang.String getMemoryOptimizerTargetNodeNameScope() { java.lang.Object ref = memoryOptimizerTargetNodeNameScope_; if (ref instanceof java.lang.String) { return (java.lang.String) ref; } else { org.nd4j.shade.protobuf.ByteString bs = (org.nd4j.shade.protobuf.ByteString) ref; java.lang.String s = bs.toStringUtf8(); memoryOptimizerTargetNodeNameScope_ = s; return s; } } /** *
   * A node name scope for node names which are valid outputs of recompuations.
   * Inputs to nodes that match this scope may be recomputed (subject either to
   * manual annotation of those input nodes or to manual annotation and
   * heuristics depending on memory_optimization), but the nodes themselves will
   * not be recomputed. This matches any sub-scopes as well, meaning the scope
   * can appear not just as a top-level scope. For example, if the value is
   * "gradients/", the default, it will match node name "gradients/foo",
   * "foo/gradients/bar", but not "foo_gradients/"
   * 
* * string memory_optimizer_target_node_name_scope = 6; */ public org.nd4j.shade.protobuf.ByteString getMemoryOptimizerTargetNodeNameScopeBytes() { java.lang.Object ref = memoryOptimizerTargetNodeNameScope_; if (ref instanceof java.lang.String) { org.nd4j.shade.protobuf.ByteString b = org.nd4j.shade.protobuf.ByteString.copyFromUtf8( (java.lang.String) ref); memoryOptimizerTargetNodeNameScope_ = b; return b; } else { return (org.nd4j.shade.protobuf.ByteString) ref; } } public static final int AUTO_PARALLEL_FIELD_NUMBER = 5; private org.tensorflow.framework.AutoParallelOptions autoParallel_; /** *
   * Configures AutoParallel optimization passes either through the
   * meta-optimizer or when manually specified through the optimizers field.
   * 
* * .tensorflow.AutoParallelOptions auto_parallel = 5; */ public boolean hasAutoParallel() { return autoParallel_ != null; } /** *
   * Configures AutoParallel optimization passes either through the
   * meta-optimizer or when manually specified through the optimizers field.
   * 
* * .tensorflow.AutoParallelOptions auto_parallel = 5; */ public org.tensorflow.framework.AutoParallelOptions getAutoParallel() { return autoParallel_ == null ? org.tensorflow.framework.AutoParallelOptions.getDefaultInstance() : autoParallel_; } /** *
   * Configures AutoParallel optimization passes either through the
   * meta-optimizer or when manually specified through the optimizers field.
   * 
* * .tensorflow.AutoParallelOptions auto_parallel = 5; */ public org.tensorflow.framework.AutoParallelOptionsOrBuilder getAutoParallelOrBuilder() { return getAutoParallel(); } public static final int SCOPED_ALLOCATOR_OPTS_FIELD_NUMBER = 16; private org.tensorflow.framework.ScopedAllocatorOptions scopedAllocatorOpts_; /** * .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16; */ public boolean hasScopedAllocatorOpts() { return scopedAllocatorOpts_ != null; } /** * .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16; */ public org.tensorflow.framework.ScopedAllocatorOptions getScopedAllocatorOpts() { return scopedAllocatorOpts_ == null ? org.tensorflow.framework.ScopedAllocatorOptions.getDefaultInstance() : scopedAllocatorOpts_; } /** * .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16; */ public org.tensorflow.framework.ScopedAllocatorOptionsOrBuilder getScopedAllocatorOptsOrBuilder() { return getScopedAllocatorOpts(); } public static final int OPTIMIZERS_FIELD_NUMBER = 100; private org.nd4j.shade.protobuf.LazyStringList optimizers_; /** *
   * If non-empty, will use this as an alternative way to specify a list of
   * optimizations to turn on and the order of the optimizations (replacing the
   * meta-optimizer).
   * Of the RewriterConfig options, only the AutoParallel configuration options
   * (the auto_parallel field) apply to manually requested optimization passes
   * ("autoparallel"). Memory optimization passes ("memory") invoked here are
   * not configurable (in contrast to memory optimization passes through the
   * meta-optimizer) and act only on manual op annotations.
   * Custom registered optimizers will be run after the base optimizers, in
   * the order that they are specified.
   * 
* * repeated string optimizers = 100; */ public org.nd4j.shade.protobuf.ProtocolStringList getOptimizersList() { return optimizers_; } /** *
   * If non-empty, will use this as an alternative way to specify a list of
   * optimizations to turn on and the order of the optimizations (replacing the
   * meta-optimizer).
   * Of the RewriterConfig options, only the AutoParallel configuration options
   * (the auto_parallel field) apply to manually requested optimization passes
   * ("autoparallel"). Memory optimization passes ("memory") invoked here are
   * not configurable (in contrast to memory optimization passes through the
   * meta-optimizer) and act only on manual op annotations.
   * Custom registered optimizers will be run after the base optimizers, in
   * the order that they are specified.
   * 
* * repeated string optimizers = 100; */ public int getOptimizersCount() { return optimizers_.size(); } /** *
   * If non-empty, will use this as an alternative way to specify a list of
   * optimizations to turn on and the order of the optimizations (replacing the
   * meta-optimizer).
   * Of the RewriterConfig options, only the AutoParallel configuration options
   * (the auto_parallel field) apply to manually requested optimization passes
   * ("autoparallel"). Memory optimization passes ("memory") invoked here are
   * not configurable (in contrast to memory optimization passes through the
   * meta-optimizer) and act only on manual op annotations.
   * Custom registered optimizers will be run after the base optimizers, in
   * the order that they are specified.
   * 
* * repeated string optimizers = 100; */ public java.lang.String getOptimizers(int index) { return optimizers_.get(index); } /** *
   * If non-empty, will use this as an alternative way to specify a list of
   * optimizations to turn on and the order of the optimizations (replacing the
   * meta-optimizer).
   * Of the RewriterConfig options, only the AutoParallel configuration options
   * (the auto_parallel field) apply to manually requested optimization passes
   * ("autoparallel"). Memory optimization passes ("memory") invoked here are
   * not configurable (in contrast to memory optimization passes through the
   * meta-optimizer) and act only on manual op annotations.
   * Custom registered optimizers will be run after the base optimizers, in
   * the order that they are specified.
   * 
* * repeated string optimizers = 100; */ public org.nd4j.shade.protobuf.ByteString getOptimizersBytes(int index) { return optimizers_.getByteString(index); } public static final int CUSTOM_OPTIMIZERS_FIELD_NUMBER = 200; private java.util.List customOptimizers_; /** *
   * list of CustomGraphOptimizers to apply.
   * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public java.util.List getCustomOptimizersList() { return customOptimizers_; } /** *
   * list of CustomGraphOptimizers to apply.
   * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public java.util.List getCustomOptimizersOrBuilderList() { return customOptimizers_; } /** *
   * list of CustomGraphOptimizers to apply.
   * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public int getCustomOptimizersCount() { return customOptimizers_.size(); } /** *
   * list of CustomGraphOptimizers to apply.
   * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer getCustomOptimizers(int index) { return customOptimizers_.get(index); } /** *
   * list of CustomGraphOptimizers to apply.
   * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizerOrBuilder getCustomOptimizersOrBuilder( int index) { return customOptimizers_.get(index); } private byte memoizedIsInitialized = -1; public final boolean isInitialized() { byte isInitialized = memoizedIsInitialized; if (isInitialized == 1) return true; if (isInitialized == 0) return false; memoizedIsInitialized = 1; return true; } public void writeTo(org.nd4j.shade.protobuf.CodedOutputStream output) throws java.io.IOException { if (layoutOptimizer_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { output.writeEnum(1, layoutOptimizer_); } if (disableModelPruning_ != false) { output.writeBool(2, disableModelPruning_); } if (constantFolding_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { output.writeEnum(3, constantFolding_); } if (memoryOptimization_ != org.tensorflow.framework.RewriterConfig.MemOptType.DEFAULT_MEM_OPT.getNumber()) { output.writeEnum(4, memoryOptimization_); } if (autoParallel_ != null) { output.writeMessage(5, getAutoParallel()); } if (!getMemoryOptimizerTargetNodeNameScopeBytes().isEmpty()) { org.nd4j.shade.protobuf.GeneratedMessageV3.writeString(output, 6, memoryOptimizerTargetNodeNameScope_); } if (arithmeticOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { output.writeEnum(7, arithmeticOptimization_); } if (dependencyOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { output.writeEnum(8, dependencyOptimization_); } if (loopOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { output.writeEnum(9, loopOptimization_); } if (functionOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { output.writeEnum(10, functionOptimization_); } if (debugStripper_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { output.writeEnum(11, debugStripper_); } if (metaOptimizerIterations_ != org.tensorflow.framework.RewriterConfig.NumIterationsType.DEFAULT_NUM_ITERS.getNumber()) { output.writeEnum(12, metaOptimizerIterations_); } if (shapeOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { output.writeEnum(13, shapeOptimization_); } if (remapping_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { output.writeEnum(14, remapping_); } if (scopedAllocatorOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { output.writeEnum(15, scopedAllocatorOptimization_); } if (scopedAllocatorOpts_ != null) { output.writeMessage(16, getScopedAllocatorOpts()); } for (int i = 0; i < optimizers_.size(); i++) { org.nd4j.shade.protobuf.GeneratedMessageV3.writeString(output, 100, optimizers_.getRaw(i)); } for (int i = 0; i < customOptimizers_.size(); i++) { output.writeMessage(200, customOptimizers_.get(i)); } unknownFields.writeTo(output); } public int getSerializedSize() { int size = memoizedSize; if (size != -1) return size; size = 0; if (layoutOptimizer_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeEnumSize(1, layoutOptimizer_); } if (disableModelPruning_ != false) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeBoolSize(2, disableModelPruning_); } if (constantFolding_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeEnumSize(3, constantFolding_); } if (memoryOptimization_ != org.tensorflow.framework.RewriterConfig.MemOptType.DEFAULT_MEM_OPT.getNumber()) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeEnumSize(4, memoryOptimization_); } if (autoParallel_ != null) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeMessageSize(5, getAutoParallel()); } if (!getMemoryOptimizerTargetNodeNameScopeBytes().isEmpty()) { size += org.nd4j.shade.protobuf.GeneratedMessageV3.computeStringSize(6, memoryOptimizerTargetNodeNameScope_); } if (arithmeticOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeEnumSize(7, arithmeticOptimization_); } if (dependencyOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeEnumSize(8, dependencyOptimization_); } if (loopOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeEnumSize(9, loopOptimization_); } if (functionOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeEnumSize(10, functionOptimization_); } if (debugStripper_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeEnumSize(11, debugStripper_); } if (metaOptimizerIterations_ != org.tensorflow.framework.RewriterConfig.NumIterationsType.DEFAULT_NUM_ITERS.getNumber()) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeEnumSize(12, metaOptimizerIterations_); } if (shapeOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeEnumSize(13, shapeOptimization_); } if (remapping_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeEnumSize(14, remapping_); } if (scopedAllocatorOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeEnumSize(15, scopedAllocatorOptimization_); } if (scopedAllocatorOpts_ != null) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeMessageSize(16, getScopedAllocatorOpts()); } { int dataSize = 0; for (int i = 0; i < optimizers_.size(); i++) { dataSize += computeStringSizeNoTag(optimizers_.getRaw(i)); } size += dataSize; size += 2 * getOptimizersList().size(); } for (int i = 0; i < customOptimizers_.size(); i++) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeMessageSize(200, customOptimizers_.get(i)); } size += unknownFields.getSerializedSize(); memoizedSize = size; return size; } @java.lang.Override public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } if (!(obj instanceof org.tensorflow.framework.RewriterConfig)) { return super.equals(obj); } org.tensorflow.framework.RewriterConfig other = (org.tensorflow.framework.RewriterConfig) obj; boolean result = true; result = result && layoutOptimizer_ == other.layoutOptimizer_; result = result && constantFolding_ == other.constantFolding_; result = result && shapeOptimization_ == other.shapeOptimization_; result = result && remapping_ == other.remapping_; result = result && arithmeticOptimization_ == other.arithmeticOptimization_; result = result && dependencyOptimization_ == other.dependencyOptimization_; result = result && loopOptimization_ == other.loopOptimization_; result = result && functionOptimization_ == other.functionOptimization_; result = result && debugStripper_ == other.debugStripper_; result = result && (getDisableModelPruning() == other.getDisableModelPruning()); result = result && scopedAllocatorOptimization_ == other.scopedAllocatorOptimization_; result = result && metaOptimizerIterations_ == other.metaOptimizerIterations_; result = result && memoryOptimization_ == other.memoryOptimization_; result = result && getMemoryOptimizerTargetNodeNameScope() .equals(other.getMemoryOptimizerTargetNodeNameScope()); result = result && (hasAutoParallel() == other.hasAutoParallel()); if (hasAutoParallel()) { result = result && getAutoParallel() .equals(other.getAutoParallel()); } result = result && (hasScopedAllocatorOpts() == other.hasScopedAllocatorOpts()); if (hasScopedAllocatorOpts()) { result = result && getScopedAllocatorOpts() .equals(other.getScopedAllocatorOpts()); } result = result && getOptimizersList() .equals(other.getOptimizersList()); result = result && getCustomOptimizersList() .equals(other.getCustomOptimizersList()); result = result && unknownFields.equals(other.unknownFields); return result; } @java.lang.Override public int hashCode() { if (memoizedHashCode != 0) { return memoizedHashCode; } int hash = 41; hash = (19 * hash) + getDescriptor().hashCode(); hash = (37 * hash) + LAYOUT_OPTIMIZER_FIELD_NUMBER; hash = (53 * hash) + layoutOptimizer_; hash = (37 * hash) + CONSTANT_FOLDING_FIELD_NUMBER; hash = (53 * hash) + constantFolding_; hash = (37 * hash) + SHAPE_OPTIMIZATION_FIELD_NUMBER; hash = (53 * hash) + shapeOptimization_; hash = (37 * hash) + REMAPPING_FIELD_NUMBER; hash = (53 * hash) + remapping_; hash = (37 * hash) + ARITHMETIC_OPTIMIZATION_FIELD_NUMBER; hash = (53 * hash) + arithmeticOptimization_; hash = (37 * hash) + DEPENDENCY_OPTIMIZATION_FIELD_NUMBER; hash = (53 * hash) + dependencyOptimization_; hash = (37 * hash) + LOOP_OPTIMIZATION_FIELD_NUMBER; hash = (53 * hash) + loopOptimization_; hash = (37 * hash) + FUNCTION_OPTIMIZATION_FIELD_NUMBER; hash = (53 * hash) + functionOptimization_; hash = (37 * hash) + DEBUG_STRIPPER_FIELD_NUMBER; hash = (53 * hash) + debugStripper_; hash = (37 * hash) + DISABLE_MODEL_PRUNING_FIELD_NUMBER; hash = (53 * hash) + org.nd4j.shade.protobuf.Internal.hashBoolean( getDisableModelPruning()); hash = (37 * hash) + SCOPED_ALLOCATOR_OPTIMIZATION_FIELD_NUMBER; hash = (53 * hash) + scopedAllocatorOptimization_; hash = (37 * hash) + META_OPTIMIZER_ITERATIONS_FIELD_NUMBER; hash = (53 * hash) + metaOptimizerIterations_; hash = (37 * hash) + MEMORY_OPTIMIZATION_FIELD_NUMBER; hash = (53 * hash) + memoryOptimization_; hash = (37 * hash) + MEMORY_OPTIMIZER_TARGET_NODE_NAME_SCOPE_FIELD_NUMBER; hash = (53 * hash) + getMemoryOptimizerTargetNodeNameScope().hashCode(); if (hasAutoParallel()) { hash = (37 * hash) + AUTO_PARALLEL_FIELD_NUMBER; hash = (53 * hash) + getAutoParallel().hashCode(); } if (hasScopedAllocatorOpts()) { hash = (37 * hash) + SCOPED_ALLOCATOR_OPTS_FIELD_NUMBER; hash = (53 * hash) + getScopedAllocatorOpts().hashCode(); } if (getOptimizersCount() > 0) { hash = (37 * hash) + OPTIMIZERS_FIELD_NUMBER; hash = (53 * hash) + getOptimizersList().hashCode(); } if (getCustomOptimizersCount() > 0) { hash = (37 * hash) + CUSTOM_OPTIMIZERS_FIELD_NUMBER; hash = (53 * hash) + getCustomOptimizersList().hashCode(); } hash = (29 * hash) + unknownFields.hashCode(); memoizedHashCode = hash; return hash; } public static org.tensorflow.framework.RewriterConfig parseFrom( java.nio.ByteBuffer data) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.framework.RewriterConfig parseFrom( java.nio.ByteBuffer data, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.framework.RewriterConfig parseFrom( org.nd4j.shade.protobuf.ByteString data) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.framework.RewriterConfig parseFrom( org.nd4j.shade.protobuf.ByteString data, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.framework.RewriterConfig parseFrom(byte[] data) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.framework.RewriterConfig parseFrom( byte[] data, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.framework.RewriterConfig parseFrom(java.io.InputStream input) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } public static org.tensorflow.framework.RewriterConfig parseFrom( java.io.InputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } public static org.tensorflow.framework.RewriterConfig parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } public static org.tensorflow.framework.RewriterConfig parseDelimitedFrom( java.io.InputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } public static org.tensorflow.framework.RewriterConfig parseFrom( org.nd4j.shade.protobuf.CodedInputStream input) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } public static org.tensorflow.framework.RewriterConfig parseFrom( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } public Builder newBuilderForType() { return newBuilder(); } public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } public static Builder newBuilder(org.tensorflow.framework.RewriterConfig prototype) { return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); } public Builder toBuilder() { return this == DEFAULT_INSTANCE ? new Builder() : new Builder().mergeFrom(this); } @java.lang.Override protected Builder newBuilderForType( org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) { Builder builder = new Builder(parent); return builder; } /** *
   * Graph rewriting is experimental and subject to change, not covered by any
   * API stability guarantees.
   * 
* * Protobuf type {@code tensorflow.RewriterConfig} */ public static final class Builder extends org.nd4j.shade.protobuf.GeneratedMessageV3.Builder implements // @@protoc_insertion_point(builder_implements:tensorflow.RewriterConfig) org.tensorflow.framework.RewriterConfigOrBuilder { public static final org.nd4j.shade.protobuf.Descriptors.Descriptor getDescriptor() { return org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_descriptor; } protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { return org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_fieldAccessorTable .ensureFieldAccessorsInitialized( org.tensorflow.framework.RewriterConfig.class, org.tensorflow.framework.RewriterConfig.Builder.class); } // Construct using org.tensorflow.framework.RewriterConfig.newBuilder() private Builder() { maybeForceBuilderInitialization(); } private Builder( org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) { super(parent); maybeForceBuilderInitialization(); } private void maybeForceBuilderInitialization() { if (org.nd4j.shade.protobuf.GeneratedMessageV3 .alwaysUseFieldBuilders) { getCustomOptimizersFieldBuilder(); } } public Builder clear() { super.clear(); layoutOptimizer_ = 0; constantFolding_ = 0; shapeOptimization_ = 0; remapping_ = 0; arithmeticOptimization_ = 0; dependencyOptimization_ = 0; loopOptimization_ = 0; functionOptimization_ = 0; debugStripper_ = 0; disableModelPruning_ = false; scopedAllocatorOptimization_ = 0; metaOptimizerIterations_ = 0; memoryOptimization_ = 0; memoryOptimizerTargetNodeNameScope_ = ""; if (autoParallelBuilder_ == null) { autoParallel_ = null; } else { autoParallel_ = null; autoParallelBuilder_ = null; } if (scopedAllocatorOptsBuilder_ == null) { scopedAllocatorOpts_ = null; } else { scopedAllocatorOpts_ = null; scopedAllocatorOptsBuilder_ = null; } optimizers_ = org.nd4j.shade.protobuf.LazyStringArrayList.EMPTY; bitField0_ = (bitField0_ & ~0x00010000); if (customOptimizersBuilder_ == null) { customOptimizers_ = java.util.Collections.emptyList(); bitField0_ = (bitField0_ & ~0x00020000); } else { customOptimizersBuilder_.clear(); } return this; } public org.nd4j.shade.protobuf.Descriptors.Descriptor getDescriptorForType() { return org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_descriptor; } public org.tensorflow.framework.RewriterConfig getDefaultInstanceForType() { return org.tensorflow.framework.RewriterConfig.getDefaultInstance(); } public org.tensorflow.framework.RewriterConfig build() { org.tensorflow.framework.RewriterConfig result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } public org.tensorflow.framework.RewriterConfig buildPartial() { org.tensorflow.framework.RewriterConfig result = new org.tensorflow.framework.RewriterConfig(this); int from_bitField0_ = bitField0_; int to_bitField0_ = 0; result.layoutOptimizer_ = layoutOptimizer_; result.constantFolding_ = constantFolding_; result.shapeOptimization_ = shapeOptimization_; result.remapping_ = remapping_; result.arithmeticOptimization_ = arithmeticOptimization_; result.dependencyOptimization_ = dependencyOptimization_; result.loopOptimization_ = loopOptimization_; result.functionOptimization_ = functionOptimization_; result.debugStripper_ = debugStripper_; result.disableModelPruning_ = disableModelPruning_; result.scopedAllocatorOptimization_ = scopedAllocatorOptimization_; result.metaOptimizerIterations_ = metaOptimizerIterations_; result.memoryOptimization_ = memoryOptimization_; result.memoryOptimizerTargetNodeNameScope_ = memoryOptimizerTargetNodeNameScope_; if (autoParallelBuilder_ == null) { result.autoParallel_ = autoParallel_; } else { result.autoParallel_ = autoParallelBuilder_.build(); } if (scopedAllocatorOptsBuilder_ == null) { result.scopedAllocatorOpts_ = scopedAllocatorOpts_; } else { result.scopedAllocatorOpts_ = scopedAllocatorOptsBuilder_.build(); } if (((bitField0_ & 0x00010000) == 0x00010000)) { optimizers_ = optimizers_.getUnmodifiableView(); bitField0_ = (bitField0_ & ~0x00010000); } result.optimizers_ = optimizers_; if (customOptimizersBuilder_ == null) { if (((bitField0_ & 0x00020000) == 0x00020000)) { customOptimizers_ = java.util.Collections.unmodifiableList(customOptimizers_); bitField0_ = (bitField0_ & ~0x00020000); } result.customOptimizers_ = customOptimizers_; } else { result.customOptimizers_ = customOptimizersBuilder_.build(); } result.bitField0_ = to_bitField0_; onBuilt(); return result; } public Builder clone() { return (Builder) super.clone(); } public Builder setField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) { return (Builder) super.setField(field, value); } public Builder clearField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field) { return (Builder) super.clearField(field); } public Builder clearOneof( org.nd4j.shade.protobuf.Descriptors.OneofDescriptor oneof) { return (Builder) super.clearOneof(oneof); } public Builder setRepeatedField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value) { return (Builder) super.setRepeatedField(field, index, value); } public Builder addRepeatedField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) { return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(org.nd4j.shade.protobuf.Message other) { if (other instanceof org.tensorflow.framework.RewriterConfig) { return mergeFrom((org.tensorflow.framework.RewriterConfig)other); } else { super.mergeFrom(other); return this; } } public Builder mergeFrom(org.tensorflow.framework.RewriterConfig other) { if (other == org.tensorflow.framework.RewriterConfig.getDefaultInstance()) return this; if (other.layoutOptimizer_ != 0) { setLayoutOptimizerValue(other.getLayoutOptimizerValue()); } if (other.constantFolding_ != 0) { setConstantFoldingValue(other.getConstantFoldingValue()); } if (other.shapeOptimization_ != 0) { setShapeOptimizationValue(other.getShapeOptimizationValue()); } if (other.remapping_ != 0) { setRemappingValue(other.getRemappingValue()); } if (other.arithmeticOptimization_ != 0) { setArithmeticOptimizationValue(other.getArithmeticOptimizationValue()); } if (other.dependencyOptimization_ != 0) { setDependencyOptimizationValue(other.getDependencyOptimizationValue()); } if (other.loopOptimization_ != 0) { setLoopOptimizationValue(other.getLoopOptimizationValue()); } if (other.functionOptimization_ != 0) { setFunctionOptimizationValue(other.getFunctionOptimizationValue()); } if (other.debugStripper_ != 0) { setDebugStripperValue(other.getDebugStripperValue()); } if (other.getDisableModelPruning() != false) { setDisableModelPruning(other.getDisableModelPruning()); } if (other.scopedAllocatorOptimization_ != 0) { setScopedAllocatorOptimizationValue(other.getScopedAllocatorOptimizationValue()); } if (other.metaOptimizerIterations_ != 0) { setMetaOptimizerIterationsValue(other.getMetaOptimizerIterationsValue()); } if (other.memoryOptimization_ != 0) { setMemoryOptimizationValue(other.getMemoryOptimizationValue()); } if (!other.getMemoryOptimizerTargetNodeNameScope().isEmpty()) { memoryOptimizerTargetNodeNameScope_ = other.memoryOptimizerTargetNodeNameScope_; onChanged(); } if (other.hasAutoParallel()) { mergeAutoParallel(other.getAutoParallel()); } if (other.hasScopedAllocatorOpts()) { mergeScopedAllocatorOpts(other.getScopedAllocatorOpts()); } if (!other.optimizers_.isEmpty()) { if (optimizers_.isEmpty()) { optimizers_ = other.optimizers_; bitField0_ = (bitField0_ & ~0x00010000); } else { ensureOptimizersIsMutable(); optimizers_.addAll(other.optimizers_); } onChanged(); } if (customOptimizersBuilder_ == null) { if (!other.customOptimizers_.isEmpty()) { if (customOptimizers_.isEmpty()) { customOptimizers_ = other.customOptimizers_; bitField0_ = (bitField0_ & ~0x00020000); } else { ensureCustomOptimizersIsMutable(); customOptimizers_.addAll(other.customOptimizers_); } onChanged(); } } else { if (!other.customOptimizers_.isEmpty()) { if (customOptimizersBuilder_.isEmpty()) { customOptimizersBuilder_.dispose(); customOptimizersBuilder_ = null; customOptimizers_ = other.customOptimizers_; bitField0_ = (bitField0_ & ~0x00020000); customOptimizersBuilder_ = org.nd4j.shade.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? getCustomOptimizersFieldBuilder() : null; } else { customOptimizersBuilder_.addAllMessages(other.customOptimizers_); } } } this.mergeUnknownFields(other.unknownFields); onChanged(); return this; } public final boolean isInitialized() { return true; } public Builder mergeFrom( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { org.tensorflow.framework.RewriterConfig parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (org.nd4j.shade.protobuf.InvalidProtocolBufferException e) { parsedMessage = (org.tensorflow.framework.RewriterConfig) e.getUnfinishedMessage(); throw e.unwrapIOException(); } finally { if (parsedMessage != null) { mergeFrom(parsedMessage); } } return this; } private int bitField0_; private int layoutOptimizer_ = 0; /** *
     * Optimize tensor layouts (default is ON)
     * e.g. This will try to use NCHW layout on GPU which is faster.
     * 
* * .tensorflow.RewriterConfig.Toggle layout_optimizer = 1; */ public int getLayoutOptimizerValue() { return layoutOptimizer_; } /** *
     * Optimize tensor layouts (default is ON)
     * e.g. This will try to use NCHW layout on GPU which is faster.
     * 
* * .tensorflow.RewriterConfig.Toggle layout_optimizer = 1; */ public Builder setLayoutOptimizerValue(int value) { layoutOptimizer_ = value; onChanged(); return this; } /** *
     * Optimize tensor layouts (default is ON)
     * e.g. This will try to use NCHW layout on GPU which is faster.
     * 
* * .tensorflow.RewriterConfig.Toggle layout_optimizer = 1; */ public org.tensorflow.framework.RewriterConfig.Toggle getLayoutOptimizer() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(layoutOptimizer_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } /** *
     * Optimize tensor layouts (default is ON)
     * e.g. This will try to use NCHW layout on GPU which is faster.
     * 
* * .tensorflow.RewriterConfig.Toggle layout_optimizer = 1; */ public Builder setLayoutOptimizer(org.tensorflow.framework.RewriterConfig.Toggle value) { if (value == null) { throw new NullPointerException(); } layoutOptimizer_ = value.getNumber(); onChanged(); return this; } /** *
     * Optimize tensor layouts (default is ON)
     * e.g. This will try to use NCHW layout on GPU which is faster.
     * 
* * .tensorflow.RewriterConfig.Toggle layout_optimizer = 1; */ public Builder clearLayoutOptimizer() { layoutOptimizer_ = 0; onChanged(); return this; } private int constantFolding_ = 0; /** *
     * Fold constants (default is ON)
     * Statically infer the value of tensors when possible, and materialize the
     * result using constants.
     * 
* * .tensorflow.RewriterConfig.Toggle constant_folding = 3; */ public int getConstantFoldingValue() { return constantFolding_; } /** *
     * Fold constants (default is ON)
     * Statically infer the value of tensors when possible, and materialize the
     * result using constants.
     * 
* * .tensorflow.RewriterConfig.Toggle constant_folding = 3; */ public Builder setConstantFoldingValue(int value) { constantFolding_ = value; onChanged(); return this; } /** *
     * Fold constants (default is ON)
     * Statically infer the value of tensors when possible, and materialize the
     * result using constants.
     * 
* * .tensorflow.RewriterConfig.Toggle constant_folding = 3; */ public org.tensorflow.framework.RewriterConfig.Toggle getConstantFolding() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(constantFolding_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } /** *
     * Fold constants (default is ON)
     * Statically infer the value of tensors when possible, and materialize the
     * result using constants.
     * 
* * .tensorflow.RewriterConfig.Toggle constant_folding = 3; */ public Builder setConstantFolding(org.tensorflow.framework.RewriterConfig.Toggle value) { if (value == null) { throw new NullPointerException(); } constantFolding_ = value.getNumber(); onChanged(); return this; } /** *
     * Fold constants (default is ON)
     * Statically infer the value of tensors when possible, and materialize the
     * result using constants.
     * 
* * .tensorflow.RewriterConfig.Toggle constant_folding = 3; */ public Builder clearConstantFolding() { constantFolding_ = 0; onChanged(); return this; } private int shapeOptimization_ = 0; /** *
     * Shape optimizations (default is ON)
     * Simplify computations made on shapes.
     * 
* * .tensorflow.RewriterConfig.Toggle shape_optimization = 13; */ public int getShapeOptimizationValue() { return shapeOptimization_; } /** *
     * Shape optimizations (default is ON)
     * Simplify computations made on shapes.
     * 
* * .tensorflow.RewriterConfig.Toggle shape_optimization = 13; */ public Builder setShapeOptimizationValue(int value) { shapeOptimization_ = value; onChanged(); return this; } /** *
     * Shape optimizations (default is ON)
     * Simplify computations made on shapes.
     * 
* * .tensorflow.RewriterConfig.Toggle shape_optimization = 13; */ public org.tensorflow.framework.RewriterConfig.Toggle getShapeOptimization() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(shapeOptimization_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } /** *
     * Shape optimizations (default is ON)
     * Simplify computations made on shapes.
     * 
* * .tensorflow.RewriterConfig.Toggle shape_optimization = 13; */ public Builder setShapeOptimization(org.tensorflow.framework.RewriterConfig.Toggle value) { if (value == null) { throw new NullPointerException(); } shapeOptimization_ = value.getNumber(); onChanged(); return this; } /** *
     * Shape optimizations (default is ON)
     * Simplify computations made on shapes.
     * 
* * .tensorflow.RewriterConfig.Toggle shape_optimization = 13; */ public Builder clearShapeOptimization() { shapeOptimization_ = 0; onChanged(); return this; } private int remapping_ = 0; /** *
     * Remapping (default is ON)
     * Remap subgraphs onto more efficient implementations.
     * 
* * .tensorflow.RewriterConfig.Toggle remapping = 14; */ public int getRemappingValue() { return remapping_; } /** *
     * Remapping (default is ON)
     * Remap subgraphs onto more efficient implementations.
     * 
* * .tensorflow.RewriterConfig.Toggle remapping = 14; */ public Builder setRemappingValue(int value) { remapping_ = value; onChanged(); return this; } /** *
     * Remapping (default is ON)
     * Remap subgraphs onto more efficient implementations.
     * 
* * .tensorflow.RewriterConfig.Toggle remapping = 14; */ public org.tensorflow.framework.RewriterConfig.Toggle getRemapping() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(remapping_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } /** *
     * Remapping (default is ON)
     * Remap subgraphs onto more efficient implementations.
     * 
* * .tensorflow.RewriterConfig.Toggle remapping = 14; */ public Builder setRemapping(org.tensorflow.framework.RewriterConfig.Toggle value) { if (value == null) { throw new NullPointerException(); } remapping_ = value.getNumber(); onChanged(); return this; } /** *
     * Remapping (default is ON)
     * Remap subgraphs onto more efficient implementations.
     * 
* * .tensorflow.RewriterConfig.Toggle remapping = 14; */ public Builder clearRemapping() { remapping_ = 0; onChanged(); return this; } private int arithmeticOptimization_ = 0; /** *
     * Arithmetic optimizations (default is ON)
     * e.g. Simplify arithmetic ops; merge ops with same value (like constants).
     * 
* * .tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7; */ public int getArithmeticOptimizationValue() { return arithmeticOptimization_; } /** *
     * Arithmetic optimizations (default is ON)
     * e.g. Simplify arithmetic ops; merge ops with same value (like constants).
     * 
* * .tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7; */ public Builder setArithmeticOptimizationValue(int value) { arithmeticOptimization_ = value; onChanged(); return this; } /** *
     * Arithmetic optimizations (default is ON)
     * e.g. Simplify arithmetic ops; merge ops with same value (like constants).
     * 
* * .tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7; */ public org.tensorflow.framework.RewriterConfig.Toggle getArithmeticOptimization() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(arithmeticOptimization_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } /** *
     * Arithmetic optimizations (default is ON)
     * e.g. Simplify arithmetic ops; merge ops with same value (like constants).
     * 
* * .tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7; */ public Builder setArithmeticOptimization(org.tensorflow.framework.RewriterConfig.Toggle value) { if (value == null) { throw new NullPointerException(); } arithmeticOptimization_ = value.getNumber(); onChanged(); return this; } /** *
     * Arithmetic optimizations (default is ON)
     * e.g. Simplify arithmetic ops; merge ops with same value (like constants).
     * 
* * .tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7; */ public Builder clearArithmeticOptimization() { arithmeticOptimization_ = 0; onChanged(); return this; } private int dependencyOptimization_ = 0; /** *
     * Control dependency optimizations (default is ON).
     * Remove redundant control dependencies, which may enable other optimization.
     * 
* * .tensorflow.RewriterConfig.Toggle dependency_optimization = 8; */ public int getDependencyOptimizationValue() { return dependencyOptimization_; } /** *
     * Control dependency optimizations (default is ON).
     * Remove redundant control dependencies, which may enable other optimization.
     * 
* * .tensorflow.RewriterConfig.Toggle dependency_optimization = 8; */ public Builder setDependencyOptimizationValue(int value) { dependencyOptimization_ = value; onChanged(); return this; } /** *
     * Control dependency optimizations (default is ON).
     * Remove redundant control dependencies, which may enable other optimization.
     * 
* * .tensorflow.RewriterConfig.Toggle dependency_optimization = 8; */ public org.tensorflow.framework.RewriterConfig.Toggle getDependencyOptimization() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(dependencyOptimization_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } /** *
     * Control dependency optimizations (default is ON).
     * Remove redundant control dependencies, which may enable other optimization.
     * 
* * .tensorflow.RewriterConfig.Toggle dependency_optimization = 8; */ public Builder setDependencyOptimization(org.tensorflow.framework.RewriterConfig.Toggle value) { if (value == null) { throw new NullPointerException(); } dependencyOptimization_ = value.getNumber(); onChanged(); return this; } /** *
     * Control dependency optimizations (default is ON).
     * Remove redundant control dependencies, which may enable other optimization.
     * 
* * .tensorflow.RewriterConfig.Toggle dependency_optimization = 8; */ public Builder clearDependencyOptimization() { dependencyOptimization_ = 0; onChanged(); return this; } private int loopOptimization_ = 0; /** *
     * Loop optimizations (default is ON).
     * 
* * .tensorflow.RewriterConfig.Toggle loop_optimization = 9; */ public int getLoopOptimizationValue() { return loopOptimization_; } /** *
     * Loop optimizations (default is ON).
     * 
* * .tensorflow.RewriterConfig.Toggle loop_optimization = 9; */ public Builder setLoopOptimizationValue(int value) { loopOptimization_ = value; onChanged(); return this; } /** *
     * Loop optimizations (default is ON).
     * 
* * .tensorflow.RewriterConfig.Toggle loop_optimization = 9; */ public org.tensorflow.framework.RewriterConfig.Toggle getLoopOptimization() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(loopOptimization_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } /** *
     * Loop optimizations (default is ON).
     * 
* * .tensorflow.RewriterConfig.Toggle loop_optimization = 9; */ public Builder setLoopOptimization(org.tensorflow.framework.RewriterConfig.Toggle value) { if (value == null) { throw new NullPointerException(); } loopOptimization_ = value.getNumber(); onChanged(); return this; } /** *
     * Loop optimizations (default is ON).
     * 
* * .tensorflow.RewriterConfig.Toggle loop_optimization = 9; */ public Builder clearLoopOptimization() { loopOptimization_ = 0; onChanged(); return this; } private int functionOptimization_ = 0; /** *
     * Function optimizations (default is ON).
     * 
* * .tensorflow.RewriterConfig.Toggle function_optimization = 10; */ public int getFunctionOptimizationValue() { return functionOptimization_; } /** *
     * Function optimizations (default is ON).
     * 
* * .tensorflow.RewriterConfig.Toggle function_optimization = 10; */ public Builder setFunctionOptimizationValue(int value) { functionOptimization_ = value; onChanged(); return this; } /** *
     * Function optimizations (default is ON).
     * 
* * .tensorflow.RewriterConfig.Toggle function_optimization = 10; */ public org.tensorflow.framework.RewriterConfig.Toggle getFunctionOptimization() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(functionOptimization_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } /** *
     * Function optimizations (default is ON).
     * 
* * .tensorflow.RewriterConfig.Toggle function_optimization = 10; */ public Builder setFunctionOptimization(org.tensorflow.framework.RewriterConfig.Toggle value) { if (value == null) { throw new NullPointerException(); } functionOptimization_ = value.getNumber(); onChanged(); return this; } /** *
     * Function optimizations (default is ON).
     * 
* * .tensorflow.RewriterConfig.Toggle function_optimization = 10; */ public Builder clearFunctionOptimization() { functionOptimization_ = 0; onChanged(); return this; } private int debugStripper_ = 0; /** *
     * Strips debug-related nodes from the graph (off by default).
     * 
* * .tensorflow.RewriterConfig.Toggle debug_stripper = 11; */ public int getDebugStripperValue() { return debugStripper_; } /** *
     * Strips debug-related nodes from the graph (off by default).
     * 
* * .tensorflow.RewriterConfig.Toggle debug_stripper = 11; */ public Builder setDebugStripperValue(int value) { debugStripper_ = value; onChanged(); return this; } /** *
     * Strips debug-related nodes from the graph (off by default).
     * 
* * .tensorflow.RewriterConfig.Toggle debug_stripper = 11; */ public org.tensorflow.framework.RewriterConfig.Toggle getDebugStripper() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(debugStripper_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } /** *
     * Strips debug-related nodes from the graph (off by default).
     * 
* * .tensorflow.RewriterConfig.Toggle debug_stripper = 11; */ public Builder setDebugStripper(org.tensorflow.framework.RewriterConfig.Toggle value) { if (value == null) { throw new NullPointerException(); } debugStripper_ = value.getNumber(); onChanged(); return this; } /** *
     * Strips debug-related nodes from the graph (off by default).
     * 
* * .tensorflow.RewriterConfig.Toggle debug_stripper = 11; */ public Builder clearDebugStripper() { debugStripper_ = 0; onChanged(); return this; } private boolean disableModelPruning_ ; /** *
     * If true, don't remove unnecessary ops from the graph
     * 
* * bool disable_model_pruning = 2; */ public boolean getDisableModelPruning() { return disableModelPruning_; } /** *
     * If true, don't remove unnecessary ops from the graph
     * 
* * bool disable_model_pruning = 2; */ public Builder setDisableModelPruning(boolean value) { disableModelPruning_ = value; onChanged(); return this; } /** *
     * If true, don't remove unnecessary ops from the graph
     * 
* * bool disable_model_pruning = 2; */ public Builder clearDisableModelPruning() { disableModelPruning_ = false; onChanged(); return this; } private int scopedAllocatorOptimization_ = 0; /** *
     * Try to allocate some independent Op outputs contiguously in order to
     * merge or eliminate downstream Ops (off by default).
     * 
* * .tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15; */ public int getScopedAllocatorOptimizationValue() { return scopedAllocatorOptimization_; } /** *
     * Try to allocate some independent Op outputs contiguously in order to
     * merge or eliminate downstream Ops (off by default).
     * 
* * .tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15; */ public Builder setScopedAllocatorOptimizationValue(int value) { scopedAllocatorOptimization_ = value; onChanged(); return this; } /** *
     * Try to allocate some independent Op outputs contiguously in order to
     * merge or eliminate downstream Ops (off by default).
     * 
* * .tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15; */ public org.tensorflow.framework.RewriterConfig.Toggle getScopedAllocatorOptimization() { org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(scopedAllocatorOptimization_); return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result; } /** *
     * Try to allocate some independent Op outputs contiguously in order to
     * merge or eliminate downstream Ops (off by default).
     * 
* * .tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15; */ public Builder setScopedAllocatorOptimization(org.tensorflow.framework.RewriterConfig.Toggle value) { if (value == null) { throw new NullPointerException(); } scopedAllocatorOptimization_ = value.getNumber(); onChanged(); return this; } /** *
     * Try to allocate some independent Op outputs contiguously in order to
     * merge or eliminate downstream Ops (off by default).
     * 
* * .tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15; */ public Builder clearScopedAllocatorOptimization() { scopedAllocatorOptimization_ = 0; onChanged(); return this; } private int metaOptimizerIterations_ = 0; /** *
     * Controls how many times we run the optimizers in meta optimizer (default
     * is once).
     * 
* * .tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12; */ public int getMetaOptimizerIterationsValue() { return metaOptimizerIterations_; } /** *
     * Controls how many times we run the optimizers in meta optimizer (default
     * is once).
     * 
* * .tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12; */ public Builder setMetaOptimizerIterationsValue(int value) { metaOptimizerIterations_ = value; onChanged(); return this; } /** *
     * Controls how many times we run the optimizers in meta optimizer (default
     * is once).
     * 
* * .tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12; */ public org.tensorflow.framework.RewriterConfig.NumIterationsType getMetaOptimizerIterations() { org.tensorflow.framework.RewriterConfig.NumIterationsType result = org.tensorflow.framework.RewriterConfig.NumIterationsType.valueOf(metaOptimizerIterations_); return result == null ? org.tensorflow.framework.RewriterConfig.NumIterationsType.UNRECOGNIZED : result; } /** *
     * Controls how many times we run the optimizers in meta optimizer (default
     * is once).
     * 
* * .tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12; */ public Builder setMetaOptimizerIterations(org.tensorflow.framework.RewriterConfig.NumIterationsType value) { if (value == null) { throw new NullPointerException(); } metaOptimizerIterations_ = value.getNumber(); onChanged(); return this; } /** *
     * Controls how many times we run the optimizers in meta optimizer (default
     * is once).
     * 
* * .tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12; */ public Builder clearMetaOptimizerIterations() { metaOptimizerIterations_ = 0; onChanged(); return this; } private int memoryOptimization_ = 0; /** *
     * Configures memory optimization passes through the meta-optimizer. Has no
     * effect on manually requested memory optimization passes in the optimizers
     * field.
     * 
* * .tensorflow.RewriterConfig.MemOptType memory_optimization = 4; */ public int getMemoryOptimizationValue() { return memoryOptimization_; } /** *
     * Configures memory optimization passes through the meta-optimizer. Has no
     * effect on manually requested memory optimization passes in the optimizers
     * field.
     * 
* * .tensorflow.RewriterConfig.MemOptType memory_optimization = 4; */ public Builder setMemoryOptimizationValue(int value) { memoryOptimization_ = value; onChanged(); return this; } /** *
     * Configures memory optimization passes through the meta-optimizer. Has no
     * effect on manually requested memory optimization passes in the optimizers
     * field.
     * 
* * .tensorflow.RewriterConfig.MemOptType memory_optimization = 4; */ public org.tensorflow.framework.RewriterConfig.MemOptType getMemoryOptimization() { org.tensorflow.framework.RewriterConfig.MemOptType result = org.tensorflow.framework.RewriterConfig.MemOptType.valueOf(memoryOptimization_); return result == null ? org.tensorflow.framework.RewriterConfig.MemOptType.UNRECOGNIZED : result; } /** *
     * Configures memory optimization passes through the meta-optimizer. Has no
     * effect on manually requested memory optimization passes in the optimizers
     * field.
     * 
* * .tensorflow.RewriterConfig.MemOptType memory_optimization = 4; */ public Builder setMemoryOptimization(org.tensorflow.framework.RewriterConfig.MemOptType value) { if (value == null) { throw new NullPointerException(); } memoryOptimization_ = value.getNumber(); onChanged(); return this; } /** *
     * Configures memory optimization passes through the meta-optimizer. Has no
     * effect on manually requested memory optimization passes in the optimizers
     * field.
     * 
* * .tensorflow.RewriterConfig.MemOptType memory_optimization = 4; */ public Builder clearMemoryOptimization() { memoryOptimization_ = 0; onChanged(); return this; } private java.lang.Object memoryOptimizerTargetNodeNameScope_ = ""; /** *
     * A node name scope for node names which are valid outputs of recompuations.
     * Inputs to nodes that match this scope may be recomputed (subject either to
     * manual annotation of those input nodes or to manual annotation and
     * heuristics depending on memory_optimization), but the nodes themselves will
     * not be recomputed. This matches any sub-scopes as well, meaning the scope
     * can appear not just as a top-level scope. For example, if the value is
     * "gradients/", the default, it will match node name "gradients/foo",
     * "foo/gradients/bar", but not "foo_gradients/"
     * 
* * string memory_optimizer_target_node_name_scope = 6; */ public java.lang.String getMemoryOptimizerTargetNodeNameScope() { java.lang.Object ref = memoryOptimizerTargetNodeNameScope_; if (!(ref instanceof java.lang.String)) { org.nd4j.shade.protobuf.ByteString bs = (org.nd4j.shade.protobuf.ByteString) ref; java.lang.String s = bs.toStringUtf8(); memoryOptimizerTargetNodeNameScope_ = s; return s; } else { return (java.lang.String) ref; } } /** *
     * A node name scope for node names which are valid outputs of recompuations.
     * Inputs to nodes that match this scope may be recomputed (subject either to
     * manual annotation of those input nodes or to manual annotation and
     * heuristics depending on memory_optimization), but the nodes themselves will
     * not be recomputed. This matches any sub-scopes as well, meaning the scope
     * can appear not just as a top-level scope. For example, if the value is
     * "gradients/", the default, it will match node name "gradients/foo",
     * "foo/gradients/bar", but not "foo_gradients/"
     * 
* * string memory_optimizer_target_node_name_scope = 6; */ public org.nd4j.shade.protobuf.ByteString getMemoryOptimizerTargetNodeNameScopeBytes() { java.lang.Object ref = memoryOptimizerTargetNodeNameScope_; if (ref instanceof String) { org.nd4j.shade.protobuf.ByteString b = org.nd4j.shade.protobuf.ByteString.copyFromUtf8( (java.lang.String) ref); memoryOptimizerTargetNodeNameScope_ = b; return b; } else { return (org.nd4j.shade.protobuf.ByteString) ref; } } /** *
     * A node name scope for node names which are valid outputs of recompuations.
     * Inputs to nodes that match this scope may be recomputed (subject either to
     * manual annotation of those input nodes or to manual annotation and
     * heuristics depending on memory_optimization), but the nodes themselves will
     * not be recomputed. This matches any sub-scopes as well, meaning the scope
     * can appear not just as a top-level scope. For example, if the value is
     * "gradients/", the default, it will match node name "gradients/foo",
     * "foo/gradients/bar", but not "foo_gradients/"
     * 
* * string memory_optimizer_target_node_name_scope = 6; */ public Builder setMemoryOptimizerTargetNodeNameScope( java.lang.String value) { if (value == null) { throw new NullPointerException(); } memoryOptimizerTargetNodeNameScope_ = value; onChanged(); return this; } /** *
     * A node name scope for node names which are valid outputs of recompuations.
     * Inputs to nodes that match this scope may be recomputed (subject either to
     * manual annotation of those input nodes or to manual annotation and
     * heuristics depending on memory_optimization), but the nodes themselves will
     * not be recomputed. This matches any sub-scopes as well, meaning the scope
     * can appear not just as a top-level scope. For example, if the value is
     * "gradients/", the default, it will match node name "gradients/foo",
     * "foo/gradients/bar", but not "foo_gradients/"
     * 
* * string memory_optimizer_target_node_name_scope = 6; */ public Builder clearMemoryOptimizerTargetNodeNameScope() { memoryOptimizerTargetNodeNameScope_ = getDefaultInstance().getMemoryOptimizerTargetNodeNameScope(); onChanged(); return this; } /** *
     * A node name scope for node names which are valid outputs of recompuations.
     * Inputs to nodes that match this scope may be recomputed (subject either to
     * manual annotation of those input nodes or to manual annotation and
     * heuristics depending on memory_optimization), but the nodes themselves will
     * not be recomputed. This matches any sub-scopes as well, meaning the scope
     * can appear not just as a top-level scope. For example, if the value is
     * "gradients/", the default, it will match node name "gradients/foo",
     * "foo/gradients/bar", but not "foo_gradients/"
     * 
* * string memory_optimizer_target_node_name_scope = 6; */ public Builder setMemoryOptimizerTargetNodeNameScopeBytes( org.nd4j.shade.protobuf.ByteString value) { if (value == null) { throw new NullPointerException(); } checkByteStringIsUtf8(value); memoryOptimizerTargetNodeNameScope_ = value; onChanged(); return this; } private org.tensorflow.framework.AutoParallelOptions autoParallel_ = null; private org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.AutoParallelOptions, org.tensorflow.framework.AutoParallelOptions.Builder, org.tensorflow.framework.AutoParallelOptionsOrBuilder> autoParallelBuilder_; /** *
     * Configures AutoParallel optimization passes either through the
     * meta-optimizer or when manually specified through the optimizers field.
     * 
* * .tensorflow.AutoParallelOptions auto_parallel = 5; */ public boolean hasAutoParallel() { return autoParallelBuilder_ != null || autoParallel_ != null; } /** *
     * Configures AutoParallel optimization passes either through the
     * meta-optimizer or when manually specified through the optimizers field.
     * 
* * .tensorflow.AutoParallelOptions auto_parallel = 5; */ public org.tensorflow.framework.AutoParallelOptions getAutoParallel() { if (autoParallelBuilder_ == null) { return autoParallel_ == null ? org.tensorflow.framework.AutoParallelOptions.getDefaultInstance() : autoParallel_; } else { return autoParallelBuilder_.getMessage(); } } /** *
     * Configures AutoParallel optimization passes either through the
     * meta-optimizer or when manually specified through the optimizers field.
     * 
* * .tensorflow.AutoParallelOptions auto_parallel = 5; */ public Builder setAutoParallel(org.tensorflow.framework.AutoParallelOptions value) { if (autoParallelBuilder_ == null) { if (value == null) { throw new NullPointerException(); } autoParallel_ = value; onChanged(); } else { autoParallelBuilder_.setMessage(value); } return this; } /** *
     * Configures AutoParallel optimization passes either through the
     * meta-optimizer or when manually specified through the optimizers field.
     * 
* * .tensorflow.AutoParallelOptions auto_parallel = 5; */ public Builder setAutoParallel( org.tensorflow.framework.AutoParallelOptions.Builder builderForValue) { if (autoParallelBuilder_ == null) { autoParallel_ = builderForValue.build(); onChanged(); } else { autoParallelBuilder_.setMessage(builderForValue.build()); } return this; } /** *
     * Configures AutoParallel optimization passes either through the
     * meta-optimizer or when manually specified through the optimizers field.
     * 
* * .tensorflow.AutoParallelOptions auto_parallel = 5; */ public Builder mergeAutoParallel(org.tensorflow.framework.AutoParallelOptions value) { if (autoParallelBuilder_ == null) { if (autoParallel_ != null) { autoParallel_ = org.tensorflow.framework.AutoParallelOptions.newBuilder(autoParallel_).mergeFrom(value).buildPartial(); } else { autoParallel_ = value; } onChanged(); } else { autoParallelBuilder_.mergeFrom(value); } return this; } /** *
     * Configures AutoParallel optimization passes either through the
     * meta-optimizer or when manually specified through the optimizers field.
     * 
* * .tensorflow.AutoParallelOptions auto_parallel = 5; */ public Builder clearAutoParallel() { if (autoParallelBuilder_ == null) { autoParallel_ = null; onChanged(); } else { autoParallel_ = null; autoParallelBuilder_ = null; } return this; } /** *
     * Configures AutoParallel optimization passes either through the
     * meta-optimizer or when manually specified through the optimizers field.
     * 
* * .tensorflow.AutoParallelOptions auto_parallel = 5; */ public org.tensorflow.framework.AutoParallelOptions.Builder getAutoParallelBuilder() { onChanged(); return getAutoParallelFieldBuilder().getBuilder(); } /** *
     * Configures AutoParallel optimization passes either through the
     * meta-optimizer or when manually specified through the optimizers field.
     * 
* * .tensorflow.AutoParallelOptions auto_parallel = 5; */ public org.tensorflow.framework.AutoParallelOptionsOrBuilder getAutoParallelOrBuilder() { if (autoParallelBuilder_ != null) { return autoParallelBuilder_.getMessageOrBuilder(); } else { return autoParallel_ == null ? org.tensorflow.framework.AutoParallelOptions.getDefaultInstance() : autoParallel_; } } /** *
     * Configures AutoParallel optimization passes either through the
     * meta-optimizer or when manually specified through the optimizers field.
     * 
* * .tensorflow.AutoParallelOptions auto_parallel = 5; */ private org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.AutoParallelOptions, org.tensorflow.framework.AutoParallelOptions.Builder, org.tensorflow.framework.AutoParallelOptionsOrBuilder> getAutoParallelFieldBuilder() { if (autoParallelBuilder_ == null) { autoParallelBuilder_ = new org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.AutoParallelOptions, org.tensorflow.framework.AutoParallelOptions.Builder, org.tensorflow.framework.AutoParallelOptionsOrBuilder>( getAutoParallel(), getParentForChildren(), isClean()); autoParallel_ = null; } return autoParallelBuilder_; } private org.tensorflow.framework.ScopedAllocatorOptions scopedAllocatorOpts_ = null; private org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.ScopedAllocatorOptions, org.tensorflow.framework.ScopedAllocatorOptions.Builder, org.tensorflow.framework.ScopedAllocatorOptionsOrBuilder> scopedAllocatorOptsBuilder_; /** * .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16; */ public boolean hasScopedAllocatorOpts() { return scopedAllocatorOptsBuilder_ != null || scopedAllocatorOpts_ != null; } /** * .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16; */ public org.tensorflow.framework.ScopedAllocatorOptions getScopedAllocatorOpts() { if (scopedAllocatorOptsBuilder_ == null) { return scopedAllocatorOpts_ == null ? org.tensorflow.framework.ScopedAllocatorOptions.getDefaultInstance() : scopedAllocatorOpts_; } else { return scopedAllocatorOptsBuilder_.getMessage(); } } /** * .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16; */ public Builder setScopedAllocatorOpts(org.tensorflow.framework.ScopedAllocatorOptions value) { if (scopedAllocatorOptsBuilder_ == null) { if (value == null) { throw new NullPointerException(); } scopedAllocatorOpts_ = value; onChanged(); } else { scopedAllocatorOptsBuilder_.setMessage(value); } return this; } /** * .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16; */ public Builder setScopedAllocatorOpts( org.tensorflow.framework.ScopedAllocatorOptions.Builder builderForValue) { if (scopedAllocatorOptsBuilder_ == null) { scopedAllocatorOpts_ = builderForValue.build(); onChanged(); } else { scopedAllocatorOptsBuilder_.setMessage(builderForValue.build()); } return this; } /** * .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16; */ public Builder mergeScopedAllocatorOpts(org.tensorflow.framework.ScopedAllocatorOptions value) { if (scopedAllocatorOptsBuilder_ == null) { if (scopedAllocatorOpts_ != null) { scopedAllocatorOpts_ = org.tensorflow.framework.ScopedAllocatorOptions.newBuilder(scopedAllocatorOpts_).mergeFrom(value).buildPartial(); } else { scopedAllocatorOpts_ = value; } onChanged(); } else { scopedAllocatorOptsBuilder_.mergeFrom(value); } return this; } /** * .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16; */ public Builder clearScopedAllocatorOpts() { if (scopedAllocatorOptsBuilder_ == null) { scopedAllocatorOpts_ = null; onChanged(); } else { scopedAllocatorOpts_ = null; scopedAllocatorOptsBuilder_ = null; } return this; } /** * .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16; */ public org.tensorflow.framework.ScopedAllocatorOptions.Builder getScopedAllocatorOptsBuilder() { onChanged(); return getScopedAllocatorOptsFieldBuilder().getBuilder(); } /** * .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16; */ public org.tensorflow.framework.ScopedAllocatorOptionsOrBuilder getScopedAllocatorOptsOrBuilder() { if (scopedAllocatorOptsBuilder_ != null) { return scopedAllocatorOptsBuilder_.getMessageOrBuilder(); } else { return scopedAllocatorOpts_ == null ? org.tensorflow.framework.ScopedAllocatorOptions.getDefaultInstance() : scopedAllocatorOpts_; } } /** * .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16; */ private org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.ScopedAllocatorOptions, org.tensorflow.framework.ScopedAllocatorOptions.Builder, org.tensorflow.framework.ScopedAllocatorOptionsOrBuilder> getScopedAllocatorOptsFieldBuilder() { if (scopedAllocatorOptsBuilder_ == null) { scopedAllocatorOptsBuilder_ = new org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.ScopedAllocatorOptions, org.tensorflow.framework.ScopedAllocatorOptions.Builder, org.tensorflow.framework.ScopedAllocatorOptionsOrBuilder>( getScopedAllocatorOpts(), getParentForChildren(), isClean()); scopedAllocatorOpts_ = null; } return scopedAllocatorOptsBuilder_; } private org.nd4j.shade.protobuf.LazyStringList optimizers_ = org.nd4j.shade.protobuf.LazyStringArrayList.EMPTY; private void ensureOptimizersIsMutable() { if (!((bitField0_ & 0x00010000) == 0x00010000)) { optimizers_ = new org.nd4j.shade.protobuf.LazyStringArrayList(optimizers_); bitField0_ |= 0x00010000; } } /** *
     * If non-empty, will use this as an alternative way to specify a list of
     * optimizations to turn on and the order of the optimizations (replacing the
     * meta-optimizer).
     * Of the RewriterConfig options, only the AutoParallel configuration options
     * (the auto_parallel field) apply to manually requested optimization passes
     * ("autoparallel"). Memory optimization passes ("memory") invoked here are
     * not configurable (in contrast to memory optimization passes through the
     * meta-optimizer) and act only on manual op annotations.
     * Custom registered optimizers will be run after the base optimizers, in
     * the order that they are specified.
     * 
* * repeated string optimizers = 100; */ public org.nd4j.shade.protobuf.ProtocolStringList getOptimizersList() { return optimizers_.getUnmodifiableView(); } /** *
     * If non-empty, will use this as an alternative way to specify a list of
     * optimizations to turn on and the order of the optimizations (replacing the
     * meta-optimizer).
     * Of the RewriterConfig options, only the AutoParallel configuration options
     * (the auto_parallel field) apply to manually requested optimization passes
     * ("autoparallel"). Memory optimization passes ("memory") invoked here are
     * not configurable (in contrast to memory optimization passes through the
     * meta-optimizer) and act only on manual op annotations.
     * Custom registered optimizers will be run after the base optimizers, in
     * the order that they are specified.
     * 
* * repeated string optimizers = 100; */ public int getOptimizersCount() { return optimizers_.size(); } /** *
     * If non-empty, will use this as an alternative way to specify a list of
     * optimizations to turn on and the order of the optimizations (replacing the
     * meta-optimizer).
     * Of the RewriterConfig options, only the AutoParallel configuration options
     * (the auto_parallel field) apply to manually requested optimization passes
     * ("autoparallel"). Memory optimization passes ("memory") invoked here are
     * not configurable (in contrast to memory optimization passes through the
     * meta-optimizer) and act only on manual op annotations.
     * Custom registered optimizers will be run after the base optimizers, in
     * the order that they are specified.
     * 
* * repeated string optimizers = 100; */ public java.lang.String getOptimizers(int index) { return optimizers_.get(index); } /** *
     * If non-empty, will use this as an alternative way to specify a list of
     * optimizations to turn on and the order of the optimizations (replacing the
     * meta-optimizer).
     * Of the RewriterConfig options, only the AutoParallel configuration options
     * (the auto_parallel field) apply to manually requested optimization passes
     * ("autoparallel"). Memory optimization passes ("memory") invoked here are
     * not configurable (in contrast to memory optimization passes through the
     * meta-optimizer) and act only on manual op annotations.
     * Custom registered optimizers will be run after the base optimizers, in
     * the order that they are specified.
     * 
* * repeated string optimizers = 100; */ public org.nd4j.shade.protobuf.ByteString getOptimizersBytes(int index) { return optimizers_.getByteString(index); } /** *
     * If non-empty, will use this as an alternative way to specify a list of
     * optimizations to turn on and the order of the optimizations (replacing the
     * meta-optimizer).
     * Of the RewriterConfig options, only the AutoParallel configuration options
     * (the auto_parallel field) apply to manually requested optimization passes
     * ("autoparallel"). Memory optimization passes ("memory") invoked here are
     * not configurable (in contrast to memory optimization passes through the
     * meta-optimizer) and act only on manual op annotations.
     * Custom registered optimizers will be run after the base optimizers, in
     * the order that they are specified.
     * 
* * repeated string optimizers = 100; */ public Builder setOptimizers( int index, java.lang.String value) { if (value == null) { throw new NullPointerException(); } ensureOptimizersIsMutable(); optimizers_.set(index, value); onChanged(); return this; } /** *
     * If non-empty, will use this as an alternative way to specify a list of
     * optimizations to turn on and the order of the optimizations (replacing the
     * meta-optimizer).
     * Of the RewriterConfig options, only the AutoParallel configuration options
     * (the auto_parallel field) apply to manually requested optimization passes
     * ("autoparallel"). Memory optimization passes ("memory") invoked here are
     * not configurable (in contrast to memory optimization passes through the
     * meta-optimizer) and act only on manual op annotations.
     * Custom registered optimizers will be run after the base optimizers, in
     * the order that they are specified.
     * 
* * repeated string optimizers = 100; */ public Builder addOptimizers( java.lang.String value) { if (value == null) { throw new NullPointerException(); } ensureOptimizersIsMutable(); optimizers_.add(value); onChanged(); return this; } /** *
     * If non-empty, will use this as an alternative way to specify a list of
     * optimizations to turn on and the order of the optimizations (replacing the
     * meta-optimizer).
     * Of the RewriterConfig options, only the AutoParallel configuration options
     * (the auto_parallel field) apply to manually requested optimization passes
     * ("autoparallel"). Memory optimization passes ("memory") invoked here are
     * not configurable (in contrast to memory optimization passes through the
     * meta-optimizer) and act only on manual op annotations.
     * Custom registered optimizers will be run after the base optimizers, in
     * the order that they are specified.
     * 
* * repeated string optimizers = 100; */ public Builder addAllOptimizers( java.lang.Iterable values) { ensureOptimizersIsMutable(); org.nd4j.shade.protobuf.AbstractMessageLite.Builder.addAll( values, optimizers_); onChanged(); return this; } /** *
     * If non-empty, will use this as an alternative way to specify a list of
     * optimizations to turn on and the order of the optimizations (replacing the
     * meta-optimizer).
     * Of the RewriterConfig options, only the AutoParallel configuration options
     * (the auto_parallel field) apply to manually requested optimization passes
     * ("autoparallel"). Memory optimization passes ("memory") invoked here are
     * not configurable (in contrast to memory optimization passes through the
     * meta-optimizer) and act only on manual op annotations.
     * Custom registered optimizers will be run after the base optimizers, in
     * the order that they are specified.
     * 
* * repeated string optimizers = 100; */ public Builder clearOptimizers() { optimizers_ = org.nd4j.shade.protobuf.LazyStringArrayList.EMPTY; bitField0_ = (bitField0_ & ~0x00010000); onChanged(); return this; } /** *
     * If non-empty, will use this as an alternative way to specify a list of
     * optimizations to turn on and the order of the optimizations (replacing the
     * meta-optimizer).
     * Of the RewriterConfig options, only the AutoParallel configuration options
     * (the auto_parallel field) apply to manually requested optimization passes
     * ("autoparallel"). Memory optimization passes ("memory") invoked here are
     * not configurable (in contrast to memory optimization passes through the
     * meta-optimizer) and act only on manual op annotations.
     * Custom registered optimizers will be run after the base optimizers, in
     * the order that they are specified.
     * 
* * repeated string optimizers = 100; */ public Builder addOptimizersBytes( org.nd4j.shade.protobuf.ByteString value) { if (value == null) { throw new NullPointerException(); } checkByteStringIsUtf8(value); ensureOptimizersIsMutable(); optimizers_.add(value); onChanged(); return this; } private java.util.List customOptimizers_ = java.util.Collections.emptyList(); private void ensureCustomOptimizersIsMutable() { if (!((bitField0_ & 0x00020000) == 0x00020000)) { customOptimizers_ = new java.util.ArrayList(customOptimizers_); bitField0_ |= 0x00020000; } } private org.nd4j.shade.protobuf.RepeatedFieldBuilderV3< org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizerOrBuilder> customOptimizersBuilder_; /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public java.util.List getCustomOptimizersList() { if (customOptimizersBuilder_ == null) { return java.util.Collections.unmodifiableList(customOptimizers_); } else { return customOptimizersBuilder_.getMessageList(); } } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public int getCustomOptimizersCount() { if (customOptimizersBuilder_ == null) { return customOptimizers_.size(); } else { return customOptimizersBuilder_.getCount(); } } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer getCustomOptimizers(int index) { if (customOptimizersBuilder_ == null) { return customOptimizers_.get(index); } else { return customOptimizersBuilder_.getMessage(index); } } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public Builder setCustomOptimizers( int index, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer value) { if (customOptimizersBuilder_ == null) { if (value == null) { throw new NullPointerException(); } ensureCustomOptimizersIsMutable(); customOptimizers_.set(index, value); onChanged(); } else { customOptimizersBuilder_.setMessage(index, value); } return this; } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public Builder setCustomOptimizers( int index, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder builderForValue) { if (customOptimizersBuilder_ == null) { ensureCustomOptimizersIsMutable(); customOptimizers_.set(index, builderForValue.build()); onChanged(); } else { customOptimizersBuilder_.setMessage(index, builderForValue.build()); } return this; } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public Builder addCustomOptimizers(org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer value) { if (customOptimizersBuilder_ == null) { if (value == null) { throw new NullPointerException(); } ensureCustomOptimizersIsMutable(); customOptimizers_.add(value); onChanged(); } else { customOptimizersBuilder_.addMessage(value); } return this; } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public Builder addCustomOptimizers( int index, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer value) { if (customOptimizersBuilder_ == null) { if (value == null) { throw new NullPointerException(); } ensureCustomOptimizersIsMutable(); customOptimizers_.add(index, value); onChanged(); } else { customOptimizersBuilder_.addMessage(index, value); } return this; } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public Builder addCustomOptimizers( org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder builderForValue) { if (customOptimizersBuilder_ == null) { ensureCustomOptimizersIsMutable(); customOptimizers_.add(builderForValue.build()); onChanged(); } else { customOptimizersBuilder_.addMessage(builderForValue.build()); } return this; } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public Builder addCustomOptimizers( int index, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder builderForValue) { if (customOptimizersBuilder_ == null) { ensureCustomOptimizersIsMutable(); customOptimizers_.add(index, builderForValue.build()); onChanged(); } else { customOptimizersBuilder_.addMessage(index, builderForValue.build()); } return this; } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public Builder addAllCustomOptimizers( java.lang.Iterable values) { if (customOptimizersBuilder_ == null) { ensureCustomOptimizersIsMutable(); org.nd4j.shade.protobuf.AbstractMessageLite.Builder.addAll( values, customOptimizers_); onChanged(); } else { customOptimizersBuilder_.addAllMessages(values); } return this; } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public Builder clearCustomOptimizers() { if (customOptimizersBuilder_ == null) { customOptimizers_ = java.util.Collections.emptyList(); bitField0_ = (bitField0_ & ~0x00020000); onChanged(); } else { customOptimizersBuilder_.clear(); } return this; } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public Builder removeCustomOptimizers(int index) { if (customOptimizersBuilder_ == null) { ensureCustomOptimizersIsMutable(); customOptimizers_.remove(index); onChanged(); } else { customOptimizersBuilder_.remove(index); } return this; } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder getCustomOptimizersBuilder( int index) { return getCustomOptimizersFieldBuilder().getBuilder(index); } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizerOrBuilder getCustomOptimizersOrBuilder( int index) { if (customOptimizersBuilder_ == null) { return customOptimizers_.get(index); } else { return customOptimizersBuilder_.getMessageOrBuilder(index); } } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public java.util.List getCustomOptimizersOrBuilderList() { if (customOptimizersBuilder_ != null) { return customOptimizersBuilder_.getMessageOrBuilderList(); } else { return java.util.Collections.unmodifiableList(customOptimizers_); } } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder addCustomOptimizersBuilder() { return getCustomOptimizersFieldBuilder().addBuilder( org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.getDefaultInstance()); } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder addCustomOptimizersBuilder( int index) { return getCustomOptimizersFieldBuilder().addBuilder( index, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.getDefaultInstance()); } /** *
     * list of CustomGraphOptimizers to apply.
     * 
* * repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200; */ public java.util.List getCustomOptimizersBuilderList() { return getCustomOptimizersFieldBuilder().getBuilderList(); } private org.nd4j.shade.protobuf.RepeatedFieldBuilderV3< org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizerOrBuilder> getCustomOptimizersFieldBuilder() { if (customOptimizersBuilder_ == null) { customOptimizersBuilder_ = new org.nd4j.shade.protobuf.RepeatedFieldBuilderV3< org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizerOrBuilder>( customOptimizers_, ((bitField0_ & 0x00020000) == 0x00020000), getParentForChildren(), isClean()); customOptimizers_ = null; } return customOptimizersBuilder_; } public final Builder setUnknownFields( final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) { return super.setUnknownFieldsProto3(unknownFields); } public final Builder mergeUnknownFields( final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) { return super.mergeUnknownFields(unknownFields); } // @@protoc_insertion_point(builder_scope:tensorflow.RewriterConfig) } // @@protoc_insertion_point(class_scope:tensorflow.RewriterConfig) private static final org.tensorflow.framework.RewriterConfig DEFAULT_INSTANCE; static { DEFAULT_INSTANCE = new org.tensorflow.framework.RewriterConfig(); } public static org.tensorflow.framework.RewriterConfig getDefaultInstance() { return DEFAULT_INSTANCE; } private static final org.nd4j.shade.protobuf.Parser PARSER = new org.nd4j.shade.protobuf.AbstractParser() { public RewriterConfig parsePartialFrom( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return new RewriterConfig(input, extensionRegistry); } }; public static org.nd4j.shade.protobuf.Parser parser() { return PARSER; } @java.lang.Override public org.nd4j.shade.protobuf.Parser getParserForType() { return PARSER; } public org.tensorflow.framework.RewriterConfig getDefaultInstanceForType() { return DEFAULT_INSTANCE; } }




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