namespace tensorflow {

// Builds and returns a `TracingContext` using the default tracing impl.
AbstractContext* BuildFunction(const char* fn_name);

// Creates parameters (placeholders) in the tracing `ctx` using the shape and
// dtype of `inputs`.
Status CreateParamsForInputs(AbstractContext* ctx,
                             absl::Span<AbstractTensorHandle* const> inputs,

      : forward_outputs_(f_outputs) {
    for (auto output : forward_outputs_) {
      if (output) {
        output->Ref();
      }
    }
  }

  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    AbstractTensorHandle* upstream_grad = grad_outputs[0];

namespace internal {
namespace {

using tensorflow::TF_StatusPtr;

Status ReluModel(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> inputs,
                 absl::Span<AbstractTensorHandle*> outputs) {
  return ops::Relu(ctx, inputs[0], &outputs[0], "Relu");
}

Status SparseSoftmaxCrossEntropyWithLogitsModel(
    AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs,

    Status s = StatusFromTF_Status(status.get());
    CHECK_EQ(errors::OK, s.code()) << s.message();
  }
};

class PassThroughGradientFunction : public GradientFunction {
 public:
  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    CHECK_EQ(grad_outputs.size(), 1);

}

TEST_P(CppGradients, TestSetAttrString) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  AbstractContextPtr ctx;
  {
    AbstractContext* ctx_raw = nullptr;
    Status s =
        BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    ctx.reset(ctx_raw);
  }

using tensorflow::ops::Mul;
using tensorflow::ops::Neg;
using tensorflow::ops::OnesLike;
using tensorflow::ops::SqrtGrad;

namespace tensorflow {
namespace gradients {
namespace {

static Status SafeConj(AbstractContext* ctx, AbstractTensorHandle* const input,
                       AbstractTensorHandle** output, const char* name) {
  auto dtype = input->DataType();
  if (DataTypeIsFloating(BaseType(dtype)) ||

#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace gradients {
namespace internal {
namespace {

using tensorflow::TF_StatusPtr;

Status IdentityNModel(AbstractContext* ctx,
                      absl::Span<AbstractTensorHandle* const> inputs,
                      absl::Span<AbstractTensorHandle*> outputs) {
  std::vector<AbstractTensorHandle*> temp_outputs(2);
  TF_RETURN_IF_ERROR(

 public:
  bool UseMlir() const { return strcmp(std::get<0>(GetParam()), "mlir") == 0; }
  bool UseFunction() const { return std::get<2>(GetParam()); }
};

// Checks that inputs[0] is a scalar.
Status TestScalarShape(AbstractContext* ctx,
                       absl::Span<AbstractTensorHandle* const> inputs,
                       absl::Span<AbstractTensorHandle*> outputs) {
  PartialTensorShape shape;
  TF_RETURN_IF_ERROR(inputs[0]->Shape(&shape));

#include "tensorflow/core/platform/errors.h"

namespace tensorflow {

AbstractContext* BuildFunction(const char* fn_name) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TF_ExecutionContext* graph_ctx = TF_CreateFunction(fn_name, status.get());
  return unwrap(graph_ctx);
}

Status CreateParamsForInputs(AbstractContext* ctx,

#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace gradients {
namespace internal {

void CompareNumericalAndAutodiffGradients(
    Model model, Model grad_model, AbstractContext* ctx,
    absl::Span<AbstractTensorHandle* const> inputs, bool use_function,
    double abs_error) {
  auto num_inputs = inputs.size();
  std::vector<AbstractTensorHandle*> outputs(num_inputs);