ShapedType packed_shape_type =
      mlir::dyn_cast<ShapedType>(packed_value.getType());
  llvm::SmallVector<int64_t> packed_shape(packed_shape_type.getShape().begin(),
                                          packed_shape_type.getShape().end());
  EXPECT_THAT(packed_shape, testing::ElementsAreArray(expected_packed_shape));
  llvm::SmallVector<int8_t> packed_value_vector(
      packed_value_attr.getValues<int8_t>());

  template <typename T>
  void MakeOp(const OpType op_type, const Tensor& x_data, const Input& y_shape,
              const bool align_corners, const bool half_pixel_centers,
              Output* x, Output* y) {
    *x = Const<T>(scope_, x_data);
    switch (op_type) {
      case RESIZE_NEAREST:
        *y = ResizeNearestNeighbor(
            scope_, *x, y_shape,
            ResizeNearestNeighbor::AlignCorners(align_corners));
        return;

  %dq_pool = "quantfork.dcast"(%q_pool) : (tensor<*x!quant.uniform<i8:f32, 0.023529411764705882:-128>>) -> tensor<*xf32>
  %reshape = "tf.Reshape"(%dq_pool, %cst) {device = ""} : (tensor<*xf32>, tensor<2xi32>) -> tensor<*xf32>
  %q_reshape = "quantfork.qcast"(%reshape) {volatile} : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 0.023529411764705882:-128>>

    } else if (op_name == "tfl.reshape" && op_state.operands.size() == 1) {
      // Special case for reshape: the second op is optional in the old
      // converter and kernel, so we create the second operand, which is
      // required by the new converter, from the reshape op's option.
      auto new_shape = op.builtin_options.AsReshapeOptions()->new_shape;
      auto shape_type = tensorflow::GetTypeFromTFTensorShape(

  Output reshape_input = ops::Placeholder(s.WithOpName("reshape_input"),
                                          DT_FLOAT, ops::Placeholder::Attrs{});
  Output reshape = ops::Reshape(s.WithOpName("reshape"), reshape_input, shape);

  AddToCluster({shape.node(), reshape.node()}, "cluster_0");

  auto graph = std::make_unique<Graph>(OpRegistry::Global());
  TF_ASSERT_OK(s.ToGraph(graph.get()));
  TF_ASSERT_OK(PartiallyDecluster(&graph));

// CHECK: %0 = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32>
// CHECK: %1 = "tfl.reshape"(%arg0, %0) : (tensor<1x4xf32>, tensor<2xi32>) -> tensor<2x2xf32>

{
  "version": 3,
  "operator_codes": [
    {
      "builtin_code": "RESHAPE"
    }
  ],
  "subgraphs": [
    {
      "tensors": [
        {
          "shape": [1, 4],
          "name": "input",

#define TENSORFLOW_C_EXPERIMENTAL_SAVED_MODEL_PUBLIC_TENSOR_SPEC_H_

#include <stddef.h>

#include "tensorflow/c/c_api_macros.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/c/tf_shape.h"

#ifdef __cplusplus
extern "C" {
#endif  // __cplusplus

// An opaque type corresponding to TensorSpec
typedef struct TF_TensorSpec TF_TensorSpec;

// Returns the dtype associated with the TensorSpec.

// CHECK-DAG:       %[[VAL_4:.*]] = "tfl.pseudo_const"(){{.*}}dense<[2, 1]> : tensor<2xi32>
// CHECK:           %[[VAL_5:.*]] = "tfl.reshape"(%[[VAL_0]], %[[VAL_2]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>
// CHECK:           %[[VAL_6:.*]] = "tfl.reshape"(%[[VAL_1]], %[[VAL_2]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>

     // `input`. In other words, the shape of the `Reshape` op are not
     // changed after the transformation.
     (IsTailOfShape $rhs, $input),
     (HasRankAtMost<4> $input),
     (HasRankAtMost<4> $lhs),
     (HasRankAtMost<4> $rhs),
     (SameElementType $input, $rhs)]>;

  // Move binary op before reshape:
  // binary(reshape(lhs), reshape(rhs)) => reshape(binary(lhs, rhs))

//
//   Slice(op, begin, size <must be constant>) =>
//     Slice(op, begin, actual_size(op.shape(), size, begin));
//       _XlaCompileTimeConstantInputs={2}
//
// where
//
//   actual_size(op_shape, size, begin)[i] =
//     size[i] == -1 ? (op_shape[i] - size[i])
//                   : size[i]
//
// This pass, combined with jit/partially_decluster_pass, reduces the number of