MemoryTypeVector input_memory_types =
      GetInputMemoryTypes(fbody, constant_arg_indices, resource_arg_indices);
  MemoryTypeVector output_memory_types = GetOutputMemoryTypes(fbody);

  // Create the kernel.
  Device* dev = flr->device();
  Status s;
  auto props = std::make_shared<NodeProperties>(
      &fbody->record->fdef().signature(), node_def, fbody->arg_types,

      xla_run_args.push_back(o);
    }
    input_idx++;
  }
  absl::c_copy(cluster_info.resource_inputs, std::back_inserter(xla_run_args));
  return xla_run_args;
}

absl::StatusOr<MemoryTypeVector> GetOutputMemoryTypes(const Scope& root,
                                                      Node* n) {
  MemoryTypeVector input_mtypes, output_mtypes;
  DeviceType device_type("");
  TF_RETURN_IF_ERROR(

// Returns output memory types.
//
// XlaLaunch kernel keeps all outputs (including constants, which it copies),
// in device memory except for resources.
tensorflow::MemoryTypeVector GetOutputMemoryTypes(
    const tensorflow::FunctionBody* fbody);

// Check whether graph can trigger XLA compilation.
bool CanTriggerXlaCompilation(const GraphDef& graph);

}  // namespace tensorflow

      // host memory.
      input_memory_types[i] = tensorflow::HOST_MEMORY;
    }
  }
  return input_memory_types;
}

tensorflow::MemoryTypeVector GetOutputMemoryTypes(
    const tensorflow::FunctionBody* fbody) {
  tensorflow::MemoryTypeVector output_memory_types(fbody->ret_types.size(),
                                                   tensorflow::DEVICE_MEMORY);