ops->read_only_memory_region_ops = static_cast<TF_ReadOnlyMemoryRegionOps*>(
      plugin_memory_allocate(TF_READ_ONLY_MEMORY_REGION_OPS_SIZE));
  ops->read_only_memory_region_ops->cleanup =
      tf_read_only_memory_region::Cleanup;
  ops->read_only_memory_region_ops->data = tf_read_only_memory_region::Data;
  ops->read_only_memory_region_ops->length = tf_read_only_memory_region::Length;

  }
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> read_op(
      TFE_NewOp(context.get(), "ReadVariableOp", status.get()), TFE_DeleteOp);
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  TFE_OpSetAttrType(read_op.get(), "dtype", TF_FLOAT);
  parallel_device.Execute(context.get(), handle_inputs, "ReadVariableOp",
                          TFE_OpGetAttrs(read_op.get()),

  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();

      dtype.code = DLDataTypeCode::kDLBool;
      break;
    case TF_DataType::TF_HALF:
    case TF_DataType::TF_FLOAT:
    case TF_DataType::TF_DOUBLE:
      dtype.code = DLDataTypeCode::kDLFloat;
      break;
    case TF_DataType::TF_INT8:
    case TF_DataType::TF_INT16:
    case TF_DataType::TF_INT32:
    case TF_DataType::TF_INT64:
      dtype.code = DLDataTypeCode::kDLInt;
      break;
    case TF_DataType::TF_UINT8:

  executed = false;
  num_retvals = 0;
  TFE_Execute(op.get(), nullptr, &num_retvals, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  ASSERT_TRUE(executed);

  // Read the variable's value.
  op.reset(TFE_NewOp(context.get(), "ReadVariableOp", status.get()));
  TFE_OpAddInput(op.get(), var_handle, status.get());
  TFE_OpSetDevice(op.get(), name, status.get());

          ->NodesToPreserve();
  char* p = static_cast<char*>(storage);

  int index = 0;
  for (const std::string& s : nodes) {
    if (index >= num_values) break;
    values[index] = p;
    lengths[index] = s.size();
    if ((p + s.size()) > (static_cast<char*>(storage) + storage_size)) {
      tsl::Set_TF_Status_from_Status(
          status,

  std::string result;
  result.resize(6);
  int64_t read = tf_random_access_file::Read(file, 0, 6, &result[0], status_);
  ASSERT_EQ(read, 6) << "Read: " << read << "\n";
  ASSERT_TF_OK(status_);
  ASSERT_EQ(result, "012345") << "Result: " << result << "\n";

  read = tf_random_access_file::Read(file, 6, 6, &result[0], status_);
  ASSERT_EQ(read, 4) << "Read: " << read << "\n";

  }

  // Read from the variable and verify that we have a parallel tensor.
  {
    TensorHandlePtr read = variable->Read(context, status.get());
    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
    std::array<TensorHandlePtr, 2> components;
    ExtractPerDeviceValues(context, read.get(), &components, status.get());

        TF_DeviceListType(devices.get(), device_index, status.get());
    ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
    if (device_type == "TPU") {
      has_tpu = true;
      break;
    }
  }
  if (has_tpu) {
    BasicTestsForTwoDevices(context.get(),
                            "/job:localhost/replica:0/task:0/device:TPU:0",

                                TF_WRITABLE_FILE_OPS_ABI, "writable file"));

  if (ops->read_only_memory_region_ops != nullptr)
    TF_RETURN_IF_ERROR(CheckABI(ops->read_only_memory_region_ops_abi,
                                TF_READ_ONLY_MEMORY_REGION_OPS_ABI,
                                "read only memory region"));

  return OkStatus();
}