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

package org.tensorflow.framework;

public interface TensorShapeProtoOrBuilder extends
    // @@protoc_insertion_point(interface_extends:tensorflow.TensorShapeProto)
    org.nd4j.shade.protobuf.MessageOrBuilder {

  /**
   * 
   * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
   * for a 30 x 40 2D tensor.  If an entry has size -1, this
   * corresponds to a dimension of unknown size. The names are
   * optional.
   * The order of entries in "dim" matters: It indicates the layout of the
   * values in the tensor in-memory representation.
   * The first entry in "dim" is the outermost dimension used to layout the
   * values, the last entry is the innermost dimension.  This matches the
   * in-memory layout of RowMajor Eigen tensors.
   * If "dim.size()" > 0, "unknown_rank" must be false.
   * 
* * repeated .tensorflow.TensorShapeProto.Dim dim = 2; */ java.util.List getDimList(); /** *
   * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
   * for a 30 x 40 2D tensor.  If an entry has size -1, this
   * corresponds to a dimension of unknown size. The names are
   * optional.
   * The order of entries in "dim" matters: It indicates the layout of the
   * values in the tensor in-memory representation.
   * The first entry in "dim" is the outermost dimension used to layout the
   * values, the last entry is the innermost dimension.  This matches the
   * in-memory layout of RowMajor Eigen tensors.
   * If "dim.size()" > 0, "unknown_rank" must be false.
   * 
* * repeated .tensorflow.TensorShapeProto.Dim dim = 2; */ org.tensorflow.framework.TensorShapeProto.Dim getDim(int index); /** *
   * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
   * for a 30 x 40 2D tensor.  If an entry has size -1, this
   * corresponds to a dimension of unknown size. The names are
   * optional.
   * The order of entries in "dim" matters: It indicates the layout of the
   * values in the tensor in-memory representation.
   * The first entry in "dim" is the outermost dimension used to layout the
   * values, the last entry is the innermost dimension.  This matches the
   * in-memory layout of RowMajor Eigen tensors.
   * If "dim.size()" > 0, "unknown_rank" must be false.
   * 
* * repeated .tensorflow.TensorShapeProto.Dim dim = 2; */ int getDimCount(); /** *
   * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
   * for a 30 x 40 2D tensor.  If an entry has size -1, this
   * corresponds to a dimension of unknown size. The names are
   * optional.
   * The order of entries in "dim" matters: It indicates the layout of the
   * values in the tensor in-memory representation.
   * The first entry in "dim" is the outermost dimension used to layout the
   * values, the last entry is the innermost dimension.  This matches the
   * in-memory layout of RowMajor Eigen tensors.
   * If "dim.size()" > 0, "unknown_rank" must be false.
   * 
* * repeated .tensorflow.TensorShapeProto.Dim dim = 2; */ java.util.List getDimOrBuilderList(); /** *
   * Dimensions of the tensor, such as {"input", 30}, {"output", 40}
   * for a 30 x 40 2D tensor.  If an entry has size -1, this
   * corresponds to a dimension of unknown size. The names are
   * optional.
   * The order of entries in "dim" matters: It indicates the layout of the
   * values in the tensor in-memory representation.
   * The first entry in "dim" is the outermost dimension used to layout the
   * values, the last entry is the innermost dimension.  This matches the
   * in-memory layout of RowMajor Eigen tensors.
   * If "dim.size()" > 0, "unknown_rank" must be false.
   * 
* * repeated .tensorflow.TensorShapeProto.Dim dim = 2; */ org.tensorflow.framework.TensorShapeProto.DimOrBuilder getDimOrBuilder( int index); /** *
   * If true, the number of dimensions in the shape is unknown.
   * If true, "dim.size()" must be 0.
   * 
* * bool unknown_rank = 3; */ boolean getUnknownRank(); }




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