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();
  }

std::vector<std::pair<string, string>> GetGradDefs(
    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) {

            ":env",
            ":logging",
            ":tf_status",
            ":tf_tensor",
            "//tensorflow/c/experimental/filesystem:modular_filesystem",
            "//tensorflow/cc:grad_ops",
            "//tensorflow/cc:gradients",
            "//tensorflow/cc:ops",
            "//tensorflow/cc:scope_internal",
            "//tensorflow/cc:while_loop",

      "Contributor" shall mean Licensor and any individual or Legal Entity
      on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable

        "//tensorflow/c:c_api",
        "//tensorflow/c:c_test_util",
        "//tensorflow/c:tf_status_helper",
        "//tensorflow/c/experimental/gradients:array_grad",
        "//tensorflow/c/experimental/gradients:math_grad",
        "//tensorflow/c/experimental/gradients:not_differentiable",
        "//tensorflow/c/experimental/gradients/tape:tape_context",
        "//tensorflow/c/experimental/ops",

==============================================================================*/
#include "tensorflow/c/experimental/gradients/math_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"

    int64_t tensor_id = source_tensor_ids[i];
    auto grad_it = gradients.find(tensor_id);
    if (grad_it == gradients.end()) {
      result[i] = nullptr;
    } else {
      if (grad_it->second.size() > 1) {
        Gradient* grad = vspace.AggregateGradients(grad_it->second);
        grad_it->second.clear();
        grad_it->second.push_back(grad);
      }
      result[i] = grad_it->second[0];
      used_gradient_ids.insert(tensor_id);

  if (TF_GetCode(status) != TF_OK) return;
  if (!grad) return;

  status->status = g->graph.AddFunctionDef(grad->record->fdef(),
                                           grad->record->stack_traces());
  if (TF_GetCode(status) != TF_OK) return;

  tensorflow::GradientDef gdef;
  gdef.set_function_name(func->record->fdef().signature().name());
  gdef.set_gradient_func(grad->record->fdef().signature().name());