mindspore2022/third_party/proto/tensorflow/tensor_shape.proto

// Protocol buffer representing the shape of tensors.

syntax = "proto3";
option cc_enable_arenas = true;
option java_outer_classname = "TensorShapeProtos";
option java_multiple_files = true;
option java_package = "org.tensorflow.framework";
option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto";

package tensorflow;

// Dimensions of a tensor.
message TensorShapeProto {
  // One dimension of the tensor.
  message Dim {
    // Size of the tensor in that dimension.
    // This value must be >= -1, but values of -1 are reserved for "unknown"
    // shapes (values of -1 mean "unknown" dimension).  Certain wrappers
    // that work with TensorShapeProto may fail at runtime when deserializing
    // a TensorShapeProto containing a dim value of -1.
    int64 size = 1;

    // Optional name of the tensor dimension.
    string name = 2;
  };

  // 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 Dim dim = 2;

  // If true, the number of dimensions in the shape is unknown.
  //
  // If true, "dim.size()" must be 0.
  bool unknown_rank = 3;
};