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);

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    float* expected_grad, int num_grad, bool use_function,
    double abs_error = 1e-2) {
  Status s;
  AbstractTensorHandlePtr numerical_grad;
  {
    AbstractTensorHandle* numerical_grad_raw;
    s = CalcNumericalGrad(ctx, model, inputs, input_index, use_function,
                          &numerical_grad_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();

namespace tensorflow {
namespace gradients {

/* Returns numerical grad inside `dtheta_approx` given `forward` model and
 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],
 * this will calculate dy/dw numerically.
 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will

//  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 {}
// };
//

Etienne Studer <******@****.***> <******@****.***>
Etienne Studer <******@****.***> <******@****.***>
Etienne Studer <******@****.***> <log>
Gary Hale <gary@gradle.com> <******@****.***>
Hans Dockter <******@****.***>
Hans Dockter <******@****.***> <******@****.***>
Hans Dockter <******@****.***> <******@****.***>

  // Define the function and its grad
  DefineFunction(func_name_, &func_);
  TF_Function* grad_func;
  DefineFunction("MyGrad", &grad_func);

  // Add func and its gradient to host graph
  TF_GraphCopyFunction(host_graph_, func_, grad_func, s_);

  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

  TF_DeleteFunction(grad_func);

  const StackTracesMap* func_stack_traces;

  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());

        "yellow":      "#ffec3d",
        "amber":       "#ffc105",
        "orange":      "#ffa724",
        "deep-orange": "#ff6e42",
        "brown":       "#795649",
        "grey":        "#757575",
        "blue-grey":   "#546d78",
        "black":       "#000000",
        "white":       "#ffffff"
      }[primary] or "#4051b5" }}
    {%- endif -%}

  # Text color (default: white)
  - &color >-

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