}
      Gradient* in_grad = in_grads[grad_index];
      if (in_grad != nullptr) {
        // ComputeGradient steals a reference
        vspace_.MarkAsResult(in_grad);
      }
      used_in_grads.push_back(in_grad);
    }
  }

  return tape->ComputeGradient(vspace_, targets, sources,
                               sources_that_are_targets, used_in_grads,
                               out_grads);

 public:
  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    CHECK_EQ(grad_outputs.size(), 1);
    CHECK_EQ(grad_inputs.size(), 1);
    grad_inputs[0] = grad_outputs[0];
    if (grad_inputs[0]) {
      grad_inputs[0]->Ref();
    }
    return absl::OkStatus();
  }
};

// Computes:
//

    const tensorflow::GraphDef& graph_def) {
  std::vector<std::pair<string, string>> grads;
  for (const tensorflow::GradientDef& grad : graph_def.library().gradient()) {
    grads.emplace_back(grad.function_name(), grad.gradient_func());
  }
  std::sort(grads.begin(), grads.end());
  return grads;
}

std::vector<string> GetFuncNames(const tensorflow::GraphDef& graph_def) {
  std::vector<string> names;

  std::vector<std::pair<string, string>> grads = GetGradDefs(gdef);
  ASSERT_EQ(2, grads.size());
  ASSERT_EQ("FooFunc1", grads[0].first);
  ASSERT_EQ("MyGrad", grads[0].second);
  ASSERT_EQ("FooFunc2", grads[1].first);
  ASSERT_EQ("MyGrad", grads[1].second);

  TF_DeleteFunction(func1);
  TF_DeleteFunction(func2);
  TF_DeleteFunction(grad_func);
}

==============================================================================*/
#include "tensorflow/c/experimental/gradients/array_grad.h"

#include "tensorflow/c/eager/c_api_test_util.h"
#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/eager/unified_api_testutil.h"
#include "tensorflow/c/experimental/gradients/grad_test_helper.h"
#include "tensorflow/c/experimental/gradients/tape/tape_context.h"

//  public:
//   Status Compute(Context* ctx,
//                  absl::Span<AbstractTensorHandle* const> grad_inputs,
//                  absl::Span<AbstractTensorHandle*> grad_outputs) override {
//     grad_outputs[0] = grad_inputs[0];
//     grad_outputs[1] = grad_inputs[0];
//     grad_outputs[0]->Ref();
//     grad_outputs[1]->Ref();
//     return OkStatus();
//   }
//   ~AddGradientFunction() override {}
// };
//

    }
  }

  TF_RETURN_IF_ERROR(GradientTape::ComputeGradient(
      vspace, target_tensor_ids, source_tensor_ids, sources_that_are_targets,
      output_gradients, result, /*build_default_zeros_grads*/ false));
  return absl::OkStatus();
}

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

                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    // Grad for Softmax Input
    TF_RETURN_IF_ERROR(BroadcastMul(
        ctx, grad_outputs[0], forward_outputs_[1],
        grad_inputs.subspan(0, 1)));  // upstream_grad * local softmax grad

    // Grad for labels is null
    grad_inputs[1] = nullptr;
    return absl::OkStatus();
  }

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);

                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    // TODO(b/161805092): Support broadcasting.

    DCHECK(grad_outputs[0]);
    grad_inputs[0] = grad_outputs[0];
    grad_inputs[1] = grad_outputs[0];

    grad_inputs[0]->Ref();
    grad_inputs[1]->Ref();
    return absl::OkStatus();
  }