// Receiver parameter types are ignored. Named (defined) types are only equal if they
// are pointer-equal - i.e. there must be a unique types.Type for each specific named
// type. Also, a type containing a shape type is considered identical to another type
// (shape or not) if their underlying types are the same, or they are both pointers.
func Identical(t1, t2 *Type) bool {
	return identical(t1, t2, 0, nil)
}

namespace tensorflow {

// Attribute marking output tensor shapes inferred by XLA. Attribute value is
// a list of PartialTensorShape objects.
extern const char kXlaInferredShapesAttrName[];

// Infers output shapes for all nodes in graph `g`. The output shapes will be
// stored in node attribute `kXlaInferredShapesAttrName`.
//
// We have to perform shape inference before encapsulation because after

  //
  // -1 indicates an unknown axis length; this is unreachable for most standard
  // ImmediateExecutionTensorHandles, but comes up for example when computing
  // the shape of a parallel tensor with component shapes differing across
  // devices.
  virtual Status Dim(int dim_index, int64_t* dim) const = 0;

  // Returns the device which created the handle.
  virtual const char* DeviceName(Status* status) const = 0;

// in LegalizeHloToTf (then from tf to tfl later).
// 3) If the operands are dynamic shaped tensors, mhlo.DynamicReshapeOp is
// inserted instead of the regular reshape, and additional ops (e.g. Gather,
// Concat ) are inserted for shape inference purposes.
// 4) All the DotOp are converted to DotGeneral during the optimization pass
// (ConvertDotOp).

// true when the args are not equal.
struct SignatureNotEqual {
  bool operator()(const Tensor& arg, const Tensor& other) {
    return arg.dtype() != other.dtype() || arg.shape() != other.shape() ||
           arg.tensor_data() != other.tensor_data();
  }
  bool operator()(const TensorTypeAndShape& arg,
                  const TensorTypeAndShape& other) {

class IdentityNOp;

// Returns the RankedTensorType for the given operand. TensorFlow constant ops
// may have non-static shape because the shape is not propagated during constant
// folding. If the defining op for the given operand is a constant op, this
// routine uses the constant op's attribute to get the actual shape.
RankedTensorType GetRankedTensorTypeForOperand(Value operand) {
  DenseElementsAttr attr;

// are specified in `variable_indices` argument. We snapshot tensors that back
// resource variables since concurrent updates may modify the shape, and it is
// important that the shapes used for compilation match the true shapes of the
// buffers.
//
// We snapshot the entire set of resource variables as one atomic operation.
// This models Read->* dependencies between resource variable operations.  See

    $mlir_module is a serialized MLIR module with a `main` function that contains target computation.
    $metadata is a serialized TPUCompileMetadataProto describing the shapes and types of the inputs to the computation, as well as a mapping onto the TPU pod topology.

 public:
  CheckpointReader(const string& filename, TF_Status* status);

  bool HasTensor(const string& name) const;
  const string DebugString() const;

  // Returns a map from variable names to their shapes.  Slices of a partitioned
  // tensor are combined into a single entry.
  const TensorSliceReader::VarToShapeMap& GetVariableToShapeMap() const;

  // Returns a map from variable names to their data types.  Slices of a

        /*batch_group_count=*/op.getBatchGroupCountAttr(),
        /*precision_config=*/op.getPrecisionConfigAttr());

    // Transpose the output of the `ConvolutionOp` back to the original op's
    // output shape so that users' shapes match.
    // [b, 0, 1, f] => [b, f, 0, 1]
    auto output_transpose_op = rewriter.create<mlir::stablehlo::TransposeOp>(
        new_convolution_op.getLoc(), /*resultType0=*/output_tensor_type,