std::vector<int64_t> in_shape{input_type.getShape().vec()};
  std::vector<int64_t> out_shape{output_type.getShape().vec()};

  // If the reshape changes the number of dimensions so it cannot be interpreted
  // as a transpose.
  if (in_shape.size() != out_shape.size()) {
    return false;
  }

  in_shape.erase(std::remove(in_shape.begin(), in_shape.end(), 1),
                 in_shape.end());

  ArrayRef<int64_t> in_shape = ranked_type.getShape();
  if (in_shape.empty() || in_shape[0] < 0) {
    return context_op->emitOpError()
           << "A map_outside_compilation op's input and output shapes must "
              "have rank at least one and the first dimension must be known.";
  }
  int64_t split_size = in_shape[0] / num_cores_per_replica;
  if (in_shape[0] % num_cores_per_replica != 0) {

TEST_F(DataFlowGradTest, DynamicStitchGrad) {
  TensorShape d1_shape({2});
  TensorShape d2_shape({2, 2});
  std::vector<Output> indices = {Const(scope_, 2), Const(scope_, {1, 0})};
  std::vector<Output> data = {
      Placeholder(scope_, DT_FLOAT, Placeholder::Shape(d1_shape)),
      Placeholder(scope_, DT_FLOAT, Placeholder::Shape(d2_shape))};
  auto y = DynamicStitch(scope_, indices, data);
  TensorShape y_shape({3, 2});

  TensorShape y_shape({3, 3, 2});
  RunTest({x}, {x_shape}, {y}, {y_shape});
}

TEST_F(LinalgGradTest, Einsum_MatMul) {
  TensorShape x_shape({2, 3});
  TensorShape y_shape({3, 3});
  Output x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
  Output y = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(y_shape));
  auto z = Einsum(scope_, {x, y}, "ij,jk->ik");
  TensorShape z_shape({2, 3});

  xs.push_back(Placeholder(scope, DT_DOUBLE, Placeholder::Shape(x_shape)));
  xs.push_back(Placeholder(scope, DT_DOUBLE, Placeholder::Shape(x_shape)));
  auto y = Stack(scope, xs, Stack::Axis(0));
  TensorShape y_shape({2, 1, 2, 3});
  double max_error;
  TF_ASSERT_OK((ComputeGradientError<double, double, double>(
      scope, xs, {x_shape, x_shape}, {y}, {y_shape}, &max_error)));
  EXPECT_LT(max_error, 1e-10);
}

#endif

// An opaque type corresponding to a shape in tensorflow. In the future,
// we may expose the ABI of TF_Shape for performance reasons.
typedef struct TF_Shape TF_Shape;

// Return a new, unknown rank shape object. The caller is responsible for
// calling TF_DeleteShape to deallocate and destroy the returned shape.
TF_CAPI_EXPORT extern TF_Shape* TF_NewShape();

// Returns the rank of `shape`. If `shape` has unknown rank, returns -1.

#include "tensorflow/c/tf_shape.h"

#include <stdint.h>

#include "tensorflow/c/tf_shape_internal.h"
#include "tensorflow/core/framework/tensor_shape.h"

extern "C" {

TF_Shape* TF_NewShape() {
  return tensorflow::wrap(new tensorflow::PartialTensorShape());
}

int TF_ShapeDims(const TF_Shape* shape) {
  return tensorflow::unwrap(shape)->dims();
}

  auto y = Sub(scope_, x1, x2);
  RunTest({x1, x2}, {x1_shape, x2_shape}, {y}, {x1_shape});
}

TEST_F(NaryGradTest, Mul) {
  TensorShape x1_shape({3, 2, 5});
  TensorShape x2_shape({2, 5});
  auto x1 = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x1_shape));
  auto x2 = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x2_shape));
  auto y = Mul(scope_, x1, x2);

  auto y = AvgPool3D(scope_, x, ksize, strides, "SAME");
  RunTest(x, x_shape, y, y_shape);
}

TEST_F(NNGradTest, LRN) {
  TensorShape x_shape({1, 1, 2, 1});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
  auto y = LRN(scope_, x);
  RunTest(x, x_shape, y, x_shape);
}

TEST_F(NNGradTest, SoftplusGrad) {
  TensorShape shape({3, 7});

  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;