tf_results->return_tensors[i].oper =
        ToOperation(results.return_tensors[i].first);
    tf_results->return_tensors[i].index = results.return_tensors[i].second;
  }

  // Populate return_nodes
  DCHECK(tf_results->return_nodes.empty());
  tf_results->return_nodes.resize(results.return_nodes.size());
  for (int i = 0; i < results.return_nodes.size(); ++i) {

    return absl::OkStatus();
  }
  ~BiasAddGradientFunction() override {}

 private:
  AttrBuilder forward_attrs_;
};

}  // namespace

GradientFunction* ReluRegisterer(const ForwardOperation& op) {
  return new ReluGradientFunction(op.outputs);
}

GradientFunction* SparseSoftmaxCrossEntropyWithLogitsRegisterer(
    const ForwardOperation& op) {

    msg += comma + factory;
    comma = ", ";
  }
  msg += ")";

  return errors::InvalidArgument(msg.c_str());
}

static TracingContext* CreateTracingExecutionContext(const char* fn_name,
                                                     TF_Status* s) {
  if (default_factory) {
    return default_factory(fn_name, s);
  }
  tsl::Set_TF_Status_from_Status(

  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 {
    return 0;
  }
}

TempFile::~TempFile() {
  std::fstream::close();
  std::remove(name_.c_str());
}

const std::string TempFile::getName() const { return name_; }

bool TempFile::truncate() {
  std::fstream::close();
  std::fstream::open(name_, std::ios::binary | std::ios::out);
  return std::fstream::is_open();

#include "tensorflow/c/tf_status.h"

// Implementation of a filesystem for POSIX environments.
// This filesystem will support `file://` and empty (local) URI schemes.

static void* plugin_memory_allocate(size_t size) { return calloc(1, size); }
static void plugin_memory_free(void* ptr) { free(ptr); }

// SECTION 1. Implementation for `TF_RandomAccessFile`
// ----------------------------------------------------------------------------

    job_def->mutable_tasks()->insert(
        {i, tensorflow::strings::StrCat("localhost", ":", port)});
    LOG(INFO) << "Picked test port: " << port << " for job: " << job_name
              << ", task: " << i;
  }
  return server_def;
}

namespace tensorflow {
namespace parallel_device {

TEST(PARALLEL_DEVICE, TestRemoteBasic) {
  std::unique_ptr<TFE_ContextOptions, decltype(&TFE_DeleteContextOptions)> opts(

        "first un-packed. Got an un-placed op with an input placed on the "
        "parallel device.");
    return;
  }
  TFE_Context* context = TFE_OpGetContext(original_op, status);
  if (TF_GetCode(status) != TF_OK) return;
  const char* operation_name = TFE_OpGetName(original_op, status);
  if (TF_GetCode(status) != TF_OK) return;
  const TFE_OpAttrs* attributes = TFE_OpGetAttrs(original_op);

  NamedParallelDevice* named_device =

    op_.reset(TFE_NewOp(context, operation_name, status));
    if (TF_GetCode(status) != TF_OK) return;
    TFE_OpSetDevice(op_.get(), device_.c_str(), status);
    if (TF_GetCode(status) != TF_OK) return;
  } else {
    TFE_OpReset(op_.get(), operation_name, device_.c_str(), status);
    if (TF_GetCode(status) != TF_OK) return;
  }
  TFE_OpAddAttrs(op_.get(), attributes);

  if (TF_GetCode(status) != TF_OK) return;
  TFE_OpAddInput(op.get(), handle_, status);
  if (TF_GetCode(status) != TF_OK) return;
  const char* device = TFE_TensorHandleDeviceName(handle_, status);
  if (TF_GetCode(status) != TF_OK) return;
  TFE_OpSetDevice(op.get(), device, status);
  if (TF_GetCode(status) != TF_OK) return;
  int num_retvals = 0;
  TFE_Execute(op.get(), nullptr, &num_retvals, status);