#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/types.h"

namespace tensorflow {

// Represents the results of the execution of an operation.
struct OutputList {
  std::vector<AbstractTensorHandle*> outputs;
  int expected_num_outputs = -1;
};

namespace tracing {

// =============================================================================

    const Scope& scope, const OutputList& xs,
    const std::vector<TensorShape>& x_shapes,
    const std::vector<Tensor>& x_datas, const OutputList& ys,
    const std::vector<TensorShape>& y_shapes,
    std::vector<Tensor>* jacobian_ts) {
  size_t y_num = y_shapes.size();
  size_t x_num = x_shapes.size();
  // Call AddSymbolicGradients to get 'dxs' (we will feed 'dys').
  OutputList dys;
  dys.reserve(y_shapes.size());

using ops::Placeholder;

class DataFlowGradTest : public ::testing::Test {
 protected:
  DataFlowGradTest() : scope_(Scope::NewRootScope()) {}

  void RunTest(const OutputList& xs, const std::vector<TensorShape>& x_shapes,
               const OutputList& ys, const std::vector<TensorShape>& y_shapes) {
    TF_ASSERT_OK(scope_.status());
    float max_error;
    TF_ASSERT_OK((ComputeGradientError<float, float, float>(

/// <X_T, Y_T, JAC_T> should be <float, complex64, float>
template <typename X_T, typename Y_T, typename JAC_T>
Status ComputeGradientError(const Scope& scope, const OutputList& xs,
                            const std::vector<TensorShape>& x_shapes,
                            const OutputList& ys,
                            const std::vector<TensorShape>& y_shapes,
                            JAC_T* max_error);

#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/graph/default_device.h"

namespace tensorflow {
namespace test {

void GetTensors(const Scope& scope, OutputList tensors,
                std::vector<Tensor>* out) {
  ClientSession session(scope);
  TF_CHECK_OK(session.Run(tensors, out));
}

void GetTensor(const Scope& scope, Output tensor, Tensor* out) {

void GetTensors(const Scope& scope, OutputList tensors,
                std::vector<Tensor>* out);

// Computes the outputs listed in 'tensors', returns the tensors in 'out'.
// assign_vars are extra outputs that should be run
// e.g. to assign values to variables.
void GetTensors(const Scope& scope, const std::vector<Output>& assign_vars,
                const OutputList& tensors, std::vector<Tensor>* out);

        scope_, {x}, {x_shape}, {y}, {y_shape}, &max_error));
    TF_CHECK_OK(result);
    TF_ASSERT_OK(result);
    EXPECT_LT(max_error, 1e-3);
  }

  void RunTest(const OutputList& xs, const std::vector<TensorShape>& x_shapes,
               const OutputList& ys, const std::vector<TensorShape>& y_shapes) {
    TF_ASSERT_OK(scope_.status());
    float max_error;
    TF_ASSERT_OK((ComputeGradientError<float, float, float>(

    TF_ASSERT_OK((ComputeGradientError<float, float, float>(
        scope_, {x}, {x_shape}, {y}, {y_shape}, &max_error)));
    EXPECT_LT(max_error, 1e-3);
  }

  void RunTest(const OutputList& xs, const std::vector<TensorShape>& x_shapes,
               const OutputList& ys, const std::vector<TensorShape>& y_shapes) {
    TF_ASSERT_OK(scope_.status());
    float max_error;
    TF_ASSERT_OK((ComputeGradientError<float, float, float>(

  auto temp = ReadVariableOp(scope, var, DT_FLOAT);

  auto y = Mul(scope, temp, x);

  auto dy = Placeholder(scope, DT_FLOAT, Placeholder::Shape(shape));

  OutputList dxs;
  TF_ASSERT_OK(AddSymbolicGradients(scope, {y}, {var}, {dy}, &dxs));

  ClientSession::FeedType feed_list;
  feed_list.insert({x, 5.0f});
  feed_list.insert({dy, 1.0f});

  std::vector<Tensor> dxout;

using tensorflow::AbstractFunction;
using tensorflow::AbstractTensorHandle;
using tensorflow::DataType;
using tensorflow::dyn_cast;
using tensorflow::OutputList;
using tensorflow::Status;
using tensorflow::unwrap;
using tensorflow::wrap;
using tensorflow::tracing::CreateTracingExecutionContext;
using tensorflow::tracing::SetDefaultTracingEngine;