namespace tensorflow {
namespace gradients {
namespace {

class ReluGradientFunction : public GradientFunction {
 public:
  explicit ReluGradientFunction(vector<AbstractTensorHandle*> f_outputs)
      : forward_outputs_(f_outputs) {
    for (auto output : forward_outputs_) {
      if (output) {
        output->Ref();
      }
    }
  }

  Status Compute(AbstractContext* ctx,

  else
    TF_SetStatus(status, TF_OK, "");
}

static void PathExists(const TF_Filesystem* filesystem, const char* path,
                       TF_Status* status) {
  if (access(path, F_OK) != 0)
    TF_SetStatusFromIOError(status, errno, path);
  else
    TF_SetStatus(status, TF_OK, "");
}

static void Stat(const TF_Filesystem* filesystem, const char* path,

    TF_RETURN_IF_ERROR(
        RunAndMaybeSum(ctx, forward, theta_inputs, f_outputs, use_function));
    AbstractTensorHandlePtr fPlus(f_outputs[0]);

    // Get f(theta - eps):
    theta_inputs[input_index] = thetaMinus.get();
    TF_RETURN_IF_ERROR(
        RunAndMaybeSum(ctx, forward, theta_inputs, f_outputs, use_function));
    AbstractTensorHandlePtr fMinus(f_outputs[0]);

};

class MatMulGradientFunction : public GradientFunction {
 public:
  explicit MatMulGradientFunction(vector<AbstractTensorHandle*> f_inputs,
                                  AttrBuilder f_attrs)
      : forward_inputs_(f_inputs), forward_attrs_(f_attrs) {
    for (auto input : forward_inputs_) {
      if (input) {
        input->Ref();
      }
    }
  }

      }
      // Float
      if (const int f_size = default_value.list().f_size()) {
        absl::InlinedVector<float, 4> f_vector;
        f_vector.reserve(f_size);
        for (int i = 0; i < f_size; ++i) {
          f_vector.push_back(default_value.list().f(i));
        }
        TFE_OpSetAttrFloatList(op, attr_name, f_vector.data(), f_size);
      }
      // Bool

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  TF_Tensor* f_t = TFE_TensorHandleResolve(final, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  float* f_value = static_cast<float*>(TF_TensorData(f_t));
  ASSERT_EQ(*f_value, 4.0);

  TF_DeleteOutputList(add_outputs);
  TF_DeleteAbstractOp(fn_op);
  TF_DeleteAbstractTensor(input_t);