auto attrs = oper->node.attrs();
  int count = 0;
  AttrValueMap::const_iterator it;
  for (it = attrs.begin(); it != attrs.end(); it++) {
    if (count == i) {
      strncpy(output, it->first.c_str(), it->first.length());
      status->status = absl::OkStatus();
      return;
    }
    count++;
  }
  status->status = OutOfRange("Operation only has ", count,

  if (dst_fd < 0) {
    close(src_fd);
    return -1;
  }

  // Both files have been opened, do the transfer.
  // Since errno would be overridden by `close` below, save it here.
  int error_code = 0;
  if (CopyFileContents(dst_fd, src_fd, size) < 0) error_code = errno;

  close(src_fd);
  close(dst_fd);
  if (error_code != 0) {
    errno = error_code;
    return -1;
  } else {

void TF_SetTracingImplementation(const char* name, TF_Status* s) {
  tsl::Set_TF_Status_from_Status(s, SetDefaultTracingEngine(name));
}

// Creates a new TensorFlow function, it is an execution context attached to a
// given tracing context.
TF_ExecutionContext* TF_CreateFunction(const char* fn_name, TF_Status* s) {
  return wrap(CreateTracingExecutionContext(fn_name, s));
}

  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
  // for computing gradient so we could safely drop it.
  outputs[0] = temp_outputs[1];
  temp_outputs[0]->Unref();
  return absl::OkStatus();
}

class CppGradients
    : public ::testing::TestWithParam<std::tuple<const char*, bool, bool>> {
 protected:

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

namespace tensorflow {

namespace {

// Managing context for the DLManagedTensor, will manage the lifetime of
// DLManagedTensor. When calling DLManagedTensor::deleter, it will notify the
// original framework of destruction, and this context will be deleted also.
struct TfDlManagedTensorCtx {
  TensorReference reference;
  std::vector<int64_t> shape;
  std::vector<int64_t> strides;

  AbstractContext* ctx_;
};

// Returns the number of elements in the gradient tensor.
int64_t TapeVSpace::NumElements(AbstractTensorHandle* tensor) const {
  // TODO(srbs): It seems like this is used only for performance optimization
  // and not for correctness. The only downside of keeping this 1 seems to be
  // that the gradient accumulation is unbounded and we will never

  if (!status->status.ok()) {
    TF_DeleteCheckpointReader(reader);
    return nullptr;
  }
  const auto& m = reader->GetVariableToDataTypeMap();
  for (auto it = m.begin(); it != m.end(); ++it)
    reader->variable_list.push_back(it->first);
  std::sort(reader->variable_list.begin(), reader->variable_list.end());
  return reader;
}

void TF_DeleteCheckpointReader(TF_CheckpointReader* reader) { delete reader; }

} PosixFile;

static void Cleanup(TF_RandomAccessFile* file) {
  auto posix_file = static_cast<PosixFile*>(file->plugin_file);
  close(posix_file->fd);
  // This would be safe to free using `free` directly as it is only opaque.
  // However, it is better to be consistent everywhere.
  plugin_memory_free(const_cast<char*>(posix_file->filename));
  delete posix_file;
}

static int64_t Read(const TF_RandomAccessFile* file, uint64_t offset, size_t n,

  TFE_ExecutorWaitForAllPendingNodes(executor, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteExecutor(executor);
  TF_DeleteStatus(status);
}

// Read the value of variable `var` and save it into `out_value`.
void ReadVariable(TFE_Context* ctx, TFE_TensorHandle* var,
                  TFE_TensorHandle** out_value) {
  TF_Status* status = TF_NewStatus();
  TFE_Op* op = TFE_NewOp(ctx, "ReadVariableOp", status);

    absl::variant<ParallelTensor*, TFE_TensorHandle*>;

// A ParallelDevice on its own is not registered with a TFE_Context, and so has
// no device name (e.g. for `tf.device`). `NamedParallelDevice` associates a
// name with it, which lets us pack its `ParallelTensor`s into TFE_TensorHandles
// placed on the parallel device.
class NamedParallelDevice {
 public:
  NamedParallelDevice(const std::string& name,