code: def send_recv_hidden(self, sending_job, receiving_job):
      st, si = sending_job if sending_job is not None else (None, None)
      rt, ri = (
          receiving_job if receiving_job is not None else (None, None)
      )
      sending = sending_job is not None and not (
          si == self.execute_gen_len - 1
          and self.pipeline_rank == self.num_pipeline_stages - 1
      )
      receiving = receiving_job is not None and not (
          ri == 0 and self.pipeline_rank == 0
      )
  
      def _send():
          sending_futures = []
          if not sending:
              return sending_futures
          for k in range(self.num_gpu_batches):
              sending_future = self.send_hidden(
                  st,
                  si,
                  self.num_layers - 1,
                  k,
                  self.sending_tag,
                  async_=self.async_comm,
              )
              sending_futures.append(sending_future)
              self.sending_tag += 1
          return sending_futures
  
      def _recv():
          receiving_futures = []
          if not receiving:
              return receiving_futures
          for k in range(self.num_gpu_batches):
              receiving_future = self.recv_hidden(
                  rt,
                  ri,
                  0,
                  k,
                  self.receiving_tag,
                  async_=self.async_comm,
              )
              receiving_futures.append(receiving_future)
              self.receiving_tag += 1
          return receiving_futures
  
      # Use special order below to avoid deadlock
      if self.pipeline_rank == 0:
          # Receive first and then send
          receiving_futures = _recv()
          sending_futures = _send()
      else:
          # Send first and then receive
          sending_futures = _send()
          receiving_futures = _recv()
      if self.async_comm:
          for sending_future in sending_futures:
              sending_future.wait()
          for receiving_future, callback in receiving_futures:
              receiving_future.wait()
              callback()
  
imports: import argparse
  from itertools import count
  import os
  import pickle
  import traceback
  from typing import Union, List, Optional
  import numpy
  import torch
  import torch.distributed
  from transformers import AutoTokenizer
  from flexgen.compression import CompressionConfig
  from flexgen.dist_utils import initialize_distributed
  from flexgen.flex_opt import Policy, InputEmbed, OutputEmbed, SelfAttention, MLP, TransformerLayer, OptLM, get_filename, add_parser_arguments, get_test_inputs, DUMMY_WEIGHT
  from flexgen.opt_config import get_opt_config
  from flexgen.pytorch_backend import TorchDevice, TorchDisk, TorchLink, TorchMixedDevice, TorchTensor
  from flexgen.timer import timers
  from flexgen.utils import Task, ExecutionEnv, GB, T, ValueHolder, array_1d, array_2d, array_3d, array_4d, str2bool, project_decode_latency
label_desc: Use image node_id 14 for calling the DistOptLM obj's underlying member method code with example usage: obj.send_recv_hidden(sending_job, receiving_job) and returns: sending_futures, receiving_futures, sending_futures, receiving_futures