#ifndef TENSORFLOW_C_EAGER_GRADIENTS_H_
#define TENSORFLOW_C_EAGER_GRADIENTS_H_

#include "absl/container/flat_hash_map.h"
#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/tape.h"
#include "tensorflow/core/common_runtime/eager/attr_builder.h"

namespace tensorflow {
namespace gradients {

limitations under the License.
==============================================================================*/

#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

#ifndef TENSORFLOW_C_EAGER_GRADIENT_CHECKER_H_
#define TENSORFLOW_C_EAGER_GRADIENT_CHECKER_H_

#include <memory>

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/unified_api_testutil.h"

namespace tensorflow {
namespace gradients {

/* Returns numerical grad inside `dtheta_approx` given `forward` model and

==============================================================================*/
#ifndef TENSORFLOW_C_EXPERIMENTAL_GRADIENTS_NOT_DIFFERENTIABLE_H_
#define TENSORFLOW_C_EXPERIMENTAL_GRADIENTS_NOT_DIFFERENTIABLE_H_

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

namespace tensorflow {
namespace gradients {
// Ignores `grad_outputs` and sets all entries in grad_inputs to nullptr.

          grad_it->second.push_back(new_gradients);
          vspace.MarkAsResult(new_gradients);
        }
        out_gradients.push_back(new_gradients);
      }
    }
    VLOG(1) << "Calling gradient function for '" << trace.op_type << "'";
    std::vector<Gradient*> in_gradients(trace.input_tensor_id.size());
    DCHECK(build_default_zeros_grads || zero_indices.empty());
    if (any_gradient_nonzero) {

#ifndef TENSORFLOW_C_EXPERIMENTAL_GRADIENTS_ARRAY_GRAD_H_
#define TENSORFLOW_C_EXPERIMENTAL_GRADIENTS_ARRAY_GRAD_H_

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

namespace tensorflow {
namespace gradients {
GradientFunction* IdentityNRegisterer(const ForwardOperation& op);
}  // namespace gradients
}  // namespace tensorflow

==============================================================================*/
#ifndef TENSORFLOW_C_EXPERIMENTAL_GRADIENTS_TAPE_TAPE_CONTEXT_H_
#define TENSORFLOW_C_EXPERIMENTAL_GRADIENTS_TAPE_TAPE_CONTEXT_H_

#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/experimental/gradients/tape/tape_operation.h"

namespace tensorflow {
namespace gradients {
class TapeContext : public AbstractContext {
 public:

==============================================================================*/
#ifndef TENSORFLOW_C_EXPERIMENTAL_GRADIENTS_TAPE_TAPE_OPERATION_H_
#define TENSORFLOW_C_EXPERIMENTAL_GRADIENTS_TAPE_TAPE_OPERATION_H_

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

namespace tensorflow {
namespace gradients {
class TapeOperation : public AbstractOperation {
 public:

// append an incremental tag to the prefix: "gradients_1/", "gradients_2/", ...
// See TF_AddGradientsWithPrefix, which provides a means to specify a custom
// name prefix for operations added to a graph to compute the gradients.
//
// WARNING: This function does not yet support all the gradients that python
// supports. See
// https://www.tensorflow.org/code/tensorflow/cc/gradients/README.md

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EXPERIMENTAL_GRADIENTS_NN_GRAD_H_
#define TENSORFLOW_C_EXPERIMENTAL_GRADIENTS_NN_GRAD_H_

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

namespace tensorflow {
namespace gradients {
GradientFunction* ReluRegisterer(const ForwardOperation& op);
GradientFunction* SparseSoftmaxCrossEntropyWithLogitsRegisterer(