void CheckBufferDataIsEqual(DataType dtype, int64_t num_elements, void* a,
                            void* b) {
  switch (dtype) {
#define CASE(type)                                       \
  case DataTypeToEnum<type>::value: {                    \
    type* typed_a = static_cast<type*>(a);               \
    type* typed_b = static_cast<type*>(b);               \
    for (int64_t i = 0; i < num_elements; ++i) {         \

    auto get_num_elements = [](RankedTensorType tensor) {
      int num_elements = 1;
      for (int i = 0; i < tensor.getRank(); ++i) {
        // Assume dynamic dim size as the dim size one.
        if (!tensor.isDynamicDim(i)) {
          num_elements *= tensor.getDimSize(i);
        }
      }
      return num_elements;
    };

    // If the op is the pass-through op with (3x) smaller output, the dequantize

    TF_RETURN_IF_ERROR(session->Create(graph));
    RunOptions run_options;

    Tensor input_a = CreateInputTensor(shape, 0);
    Tensor input_b = CreateInputTensor(shape, shape.num_elements());
    Tensor input_c = CreateInputTensor(shape, 2 * shape.num_elements());
    TF_RETURN_IF_ERROR(session->Run(
        run_options,
        {std::make_pair("a", input_a), std::make_pair("b", input_b),
         std::make_pair("c", input_c)},

  EXPECT_EQ(tensor.dtype(), dtype);
  EXPECT_EQ(*reinterpret_cast<typename TypeParam::type*>(tensor.data()), 42);
  EXPECT_EQ(tensor.num_bytes(), sizeof(typename TypeParam::type));
  EXPECT_EQ(tensor.num_elements(), 1);
}

template <typename T>
class Construct1DTensorTest : public ::testing::Test {};
TYPED_TEST_SUITE(Construct1DTensorTest, SimpleTypes);

    return {};
  }

  int64_t num_elements = value_shape.getNumElements();
  SmallVector<int64_t> new_shape;
  for (auto idx : llvm::reverse(llvm::seq<int32_t>(0, rhs_shape.getRank()))) {
    const int64_t rhs_dim = rhs_shape.getDimSize(idx);
    if (num_elements % rhs_dim != 0) {
      return {};
    }
    new_shape.push_back(rhs_dim);
    num_elements = num_elements / rhs_dim;
    if (num_elements == 1) break;
  }

class CreateStringAttr<string values> : NativeCodeCall<
  "$_builder.getStringAttr("# values #")">;

def LegalizeTensorListReserve : Pat<(TF_TensorListReserveOp:$tf_op $shape, $num_elements),
    (TFL_CustomOp (Size2InputRange $shape, $num_elements),
   (CreateStringAttr<"\"TensorListReserve\"">), (CustomOptions $tf_op))>;

def LegalizeTensorListStack : Pat<(TF_TensorListStackOp $input, $shape, $unused_num_elements),

    operand_new_shape[dimensions[i]] = operand.getType().getDimSize(i);
  }

  llvm::SmallVector<ElementValueT, 16> new_values;
  auto num_elements = result_type.getNumElements();
  new_values.reserve(num_elements);
  auto operand_values = operand.getValues<ElementValueT>();
  for (int64_t i = 0; i < num_elements; ++i) {
    const int64_t operand_index =
        GetElementIndex(operand_new_shape, current_index);

    int num_elements_threshold = quant_options_.min_num_elements_for_weights();
    int num_elements = cast<ShapedType>(op.getType()).getNumElements();
    if (num_elements < num_elements_threshold) {
      op->emitRemark("Quantization is skipped because the op has ")
          << num_elements << " elements which is fewer than the threshold("
          << num_elements_threshold << " elements).";
      return failure();

  EXPECT_EQ(tensor.dtype(), dtype);
  EXPECT_EQ(*reinterpret_cast<typename TypeParam::type*>(tensor.data()), 42);
  EXPECT_EQ(tensor.num_bytes(), sizeof(typename TypeParam::type));
  EXPECT_EQ(tensor.num_elements(), 1);
}

template <typename T>
class Construct1DTensorHandleTest : public ::testing::Test {};
TYPED_TEST_SUITE(Construct1DTensorHandleTest, SimpleTypes);

    const int64_t x_size =
        x_shapes[x_idx].num_elements() * JacobianStride<X_T>::value;
    for (int y_idx = 0; y_idx < y_num; y_idx++) {
      // The number of columns is the number of elements in the y tensor
      // multiplied by the number of Jacobian entries needed to represent each
      // y type.
      const int64_t y_size =
          y_shapes[y_idx].num_elements() * JacobianStride<Y_T>::value;