using tensorflow::PartialTensorShape;
using tensorflow::RunMetadata;
using tensorflow::RunOptions;
using tensorflow::Session;
using tensorflow::Status;
using tensorflow::string;
using tensorflow::Tensor;
using tensorflow::TensorId;
using tensorflow::TensorShapeProto;
using tensorflow::VersionDef;
using tensorflow::errors::FailedPrecondition;
using tensorflow::errors::InvalidArgument;
using tensorflow::errors::OutOfRange;

                     TF_Status* status) {
  using tensorflow::NodeDef;
  using tensorflow::OpRegistrationData;
  using tensorflow::Tensor;
  using tensorflow::shape_inference::DimensionHandle;
  using tensorflow::shape_inference::InferenceContext;
  using tensorflow::shape_inference::ShapeAndType;
  using tensorflow::shape_inference::ShapeHandle;

  const int num_inputs = input_shapes->num_items;

#include <dirent.h>
#include <errno.h>
#include <fcntl.h>
#include <limits.h>
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <sys/mman.h>
#include <sys/stat.h>
#include <unistd.h>

#include "tensorflow/c/experimental/filesystem/filesystem_interface.h"

namespace tensorflow {

std::string ImmediateExecutionTensorHandle::DebugString() const {
  PartialTensorShape shape;
  std::string shape_string;
  if (Shape(&shape).ok()) {
    shape_string = shape.DebugString();
  } else {
    shape_string = "<error computing shape>";
  }
  std::string value_string;
  if (!SummarizeValue(value_string).ok()) {
    value_string = "<error computing value>";
  }

  *result = outputs[0];
  return absl::OkStatus();
}
}  // namespace

Status GradientRegistry::Register(
    const string& op_name, GradientFunctionFactory gradient_function_factory) {
  auto iter = registry_.find(op_name);
  if (iter != registry_.end()) {
    const string error_msg = "Gradient already exists for op: " + op_name + ".";
    return errors::AlreadyExists(error_msg);
  }

limitations under the License.
==============================================================================*/

#include "tensorflow/c/eager/dlpack.h"

#include <string>

#include "include/dlpack/dlpack.h"  // from @dlpack
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"

}

class FunctionErrorInjectionPass : public tensorflow::FunctionOptimizationPass {
 public:
  FunctionErrorInjectionPass(string error_node, string error_device)
      : error_node_(error_node), error_device_(error_device) {}
  tensorflow::Status Run(const std::string& function_name,
                         const tensorflow::DeviceSet& device_set,
                         const tensorflow::ConfigProto& config_proto,

namespace tensorflow {

std::string AbstractTensorHandle::DebugString() const {
  PartialTensorShape shape;
  Status s = Shape(&shape);
  std::string shape_string;
  if (!s.ok()) {
    shape_string = "<error computing shape>";
  } else {
    shape_string = shape.DebugString();
  }
  return absl::StrCat("TensorHandle(shape=", shape_string,
                      ", dtype=", DataType_Name(DataType()),

  // Process output names.
  std::vector<string> output_names_vec;
  if (output_names) {
    output_names_vec.reserve(noutputs);
    for (int i = 0; i < noutputs; ++i) {
      output_names_vec.push_back(string(output_names[i]));
    }
  }

  // Process control output names.
  std::vector<string> control_output_names_vec;
  if (control_output_names) {

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

namespace tensorflow {
namespace gradients {
namespace internal {
namespace {
using std::vector;
using tensorflow::TF_StatusPtr;
using tracing::TracingOperation;

class CppGradients
    : public ::testing::TestWithParam<std::tuple<const char*, bool, bool>> {
 protected:
  void SetUp() override {