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});

  // The internal gradient implementation needs the shape of the input image.
  // x_shape = shape(x)[1:3]
  //         = slice(shape(x), {1}, {3 - 1})
  auto x_shape = Slice(scope, Shape(scope, op.input(0)), {1}, {2});
  grad_outputs->push_back(internal::ResizeNearestNeighborGrad(
      scope, grad_inputs[0], x_shape,
      internal::ResizeNearestNeighborGrad::AlignCorners(align_corners)

// Represents a (partially-defined) shape.
typedef struct TF_Shape {
  int num_dims;  // Must be >= -1; -1 represents unknown rank.
  int64_t* dim_sizes;
} TF_Shape;

// Add a new parameter to a TensorFlow Function.
TF_AbstractTensor* TF_AddFunctionParameter(TF_ExecutionContext* func,
                                           TF_DataType dtype, TF_Shape shape,
                                           TF_Status* s);

template <typename X_T, typename Y_T, typename JAC_T>
Status ComputeGradientError(const Scope& scope, const Output& x,
                            const Tensor& x_init_value, const Output& y,
                            const TensorShape& y_shape, JAC_T* max_error);

}  // namespace tensorflow

  TF_DeleteExecutionContext(ctx);
  return wrap(func);
}

TF_AbstractTensor* TF_AddFunctionParameter(TF_ExecutionContext* func,
                                           TF_DataType dtype, TF_Shape shape,
                                           TF_Status* s) {
  DCHECK_GE(shape.num_dims, -1);
  TracingTensorHandle* t;
  TracingContext* tracing_ctx = dyn_cast<TracingContext>(unwrap(func));
  if (!tracing_ctx) {