#include "tensorflow/c/eager/tape.h"
#include "tensorflow/core/common_runtime/eager/attr_builder.h"

namespace tensorflow {
namespace gradients {

// =============== Experimental C++ API for computing gradients ===============

// Sample gradient function:
//
// class AddGradientFunction : public GradientFunction {
//  public:
//   Status Compute(Context* ctx,

  // Looks up the ID of a Gradient.
  int64_t TensorId(AbstractTensorHandle* tensor) const override;

  // Converts a Gradient to a TapeTensor.
  TapeTensor TapeTensorFromGradient(AbstractTensorHandle* g) const override;

  void MarkAsResult(AbstractTensorHandle* gradient) const override;

  void DeleteGradient(AbstractTensorHandle* gradient) const override;

 private:

#include "tensorflow/c/eager/gradients_internal.h"
#include "tensorflow/c/eager/unified_api_testutil.h"
#include "tensorflow/c/experimental/gradients/array_grad.h"
#include "tensorflow/c/experimental/gradients/math_grad.h"
#include "tensorflow/c/experimental/gradients/not_differentiable.h"
#include "tensorflow/c/experimental/gradients/tape/tape_context.h"
#include "tensorflow/c/experimental/ops/array_ops.h"
#include "tensorflow/c/experimental/ops/math_ops.h"

#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/c/tf_tensor.h"
#include "tensorflow/core/platform/tensor_float_32_utils.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace gradients {
namespace internal {
namespace {

using tensorflow::TF_StatusPtr;

void CompareNumericalAndManualGradients(
    Model model, AbstractContext* ctx,

 * hold the numerical gradient data at the end of the function.
 */
absl::Status CalcNumericalGrad(AbstractContext* ctx, Model forward,
                               absl::Span<AbstractTensorHandle* const> inputs,
                               int input_index, bool use_function,
                               AbstractTensorHandle** numerical_grad);

}  // namespace gradients
}  // namespace tensorflow

==============================================================================*/

#ifndef TENSORFLOW_C_EAGER_GRADIENTS_INTERNAL_H_
#define TENSORFLOW_C_EAGER_GRADIENTS_INTERNAL_H_

#include "tensorflow/c/eager/gradients.h"

namespace tensorflow {
namespace gradients {
namespace internal {

// Helper functions which delegate to `AbstractOperation`, update
// the state of the ForwardOperation and call the tape as appropriate.

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/experimental/ops/math_ops.h"
#include "tensorflow/c/tf_tensor.h"

namespace tensorflow {
namespace gradients {

using namespace std;

// ================== Helper functions =================

// Fills data with values [start,end) with given step size.
void Range(vector<int32_t>* data, int32_t start, int32_t end,

  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_)) << TF_Message(s_);
}

TEST_F(CApiGradientsTest, GradientsPrefix_2ndGradientsMatchingNodeOf1st) {
  BuildGraphAndAddGradientsWithPrefixes("gradients", "gradients/MatMul");
  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_)) << TF_Message(s_);
}

TEST_F(CApiGradientsTest, GradientsPrefix_1stGradientsMatchingNodeOf2nd) {

        "//tensorflow/c/eager:tfe_context_internal",
        "//tensorflow/c/eager:tfe_op_internal",
        "//tensorflow/c/eager:tfe_tensorhandle_internal",
        "//tensorflow/c/experimental/gradients",
        "//tensorflow/c/experimental/gradients/tape",
        "//tensorflow/c/experimental/next_pluggable_device:c_api",
        "//tensorflow/c/experimental/ops",
        "//tensorflow/c:c_api_experimental",

        // (where the value starts on the host), without allowing other implicit
        // copies/broadcasts. Other implicit copies may be supported eventually,
        // but need special handling for gradients (gradient of copy-on is not
        // just copy-off but includes a sum) and consideration of performance.
        //
        // TODO(allenl): There may be smarter ways to do this copy in some