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

using std::vector;
using tensorflow::ops::BiasAddGrad;
using tensorflow::ops::Mul;
using tensorflow::ops::ReluGrad;

namespace tensorflow {
namespace gradients {
namespace {

class ReluGradientFunction : public GradientFunction {
 public:
  explicit ReluGradientFunction(vector<AbstractTensorHandle*> f_outputs)
      : forward_outputs_(f_outputs) {

    @SuppressWarnings("unused")
    @Test
    public void testIterable() throws Exception {
        final Vector<String> vector = new Vector<String>();
        vector.add("a");
        vector.add("b");
        int count = 0;
        for (final String s : iterable(vector.elements())) {
            ++count;
        }
        assertThat(count, is(2));
    }

    /**
     * Test method for

#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace gradients {
namespace internal {
namespace {
using std::vector;

class CustomGradientTest
    : public ::testing::TestWithParam<std::tuple<const char*, bool, bool>> {
 protected:
  void SetUp() override {
    TF_StatusPtr status(TF_NewStatus());

    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);
  auto s = RunModel(grad_model, ctx, inputs, absl::MakeSpan(outputs),
                    /*use_function=*/use_function);
  ASSERT_EQ(errors::OK, s.code()) << s.message();

    absl::flat_hash_set<int> null_indices;
    {
      AbstractContextPtr func_ctx(BuildFunction(fn_name));
      std::vector<AbstractTensorHandle*> func_inputs;
      func_inputs.reserve(inputs.size());
      TF_RETURN_IF_ERROR(
          CreateParamsForInputs(func_ctx.get(), inputs, &func_inputs));
      std::vector<AbstractTensorHandle*> model_outputs;
      model_outputs.resize(outputs.size());

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(
      ops::IdentityN(ctx, inputs, absl::MakeSpan(temp_outputs), "IdentityN"));
  // Although, `ops::IdentityN` returns 2 tensors, the first tensor isn't needed

// dtype of `inputs`.
Status CreateParamsForInputs(AbstractContext* ctx,
                             absl::Span<AbstractTensorHandle* const> inputs,
                             std::vector<AbstractTensorHandle*>* params);

// A callable that takes tensor inputs and returns zero or more tensor outputs.
using Model = std::function<Status(AbstractContext*,

	if err != nil {
		return 0, fmt.Errorf("query() failure for '%s': %v", query, err)
	}

	log.Debugf("executing query: %s  result:%s", query, val)

	switch v := val.(type) {
	case model.Vector:
		if v.Len() < 1 {
			log.Debugf("no values for query: %s", query)
			return 0, nil
		}

		return float64(v[0].Value), nil
	default:
		return 0, errors.New("bad metric value type returned for query")

    status_ = TestScalarTensorHandle<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), 0.0f, &Y_raw);
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();
    Y.reset(Y_raw);
  }

  std::vector<AbstractTensorHandle*> outputs(1);
  status_ = RunModel(ReluGradModel, immediate_execution_ctx_.get(), {Y.get()},
                     absl::MakeSpan(outputs), UseFunction());