TensorShapeProtoOrBuilder

 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.
 

 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.
 

 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.
 

 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.
 

 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.
 

 If true, the number of dimensions in the shape is unknown.
 If true, "dim.size()" must be 0.