code: def load_hidden(self, b, t, i, j, k):
      # Handle corner cases
      if k == self.num_gpu_batches:
          k = 0
          j += 1
      if j == self.num_layers:
          j = 0
          t += 1
      if t == self.num_inner_iterations:
          t = 0
          i += 1
      if i == self.execute_gen_len:
          i = 0
          b += 1
      if b == self.num_pipeline_batches // self.num_inner_iterations:
          return
  
      # Load to hidden states buffers
      dst = self.layers[j].compute
      if j > 0:  # load from the last layer
          val = self.hidden[t][i][j - 1][k].pop().move(dst)
          self.hidden[t][i][j][k].store(val)
          return
      if self.num_pipeline_stages > 1 and not (
          i == 0 and self.pipeline_rank == 0
      ):
          # Already received the input from previous hidden states
          self.hidden[t][i][j][k].val = self.hidden[t][i][j][
              k
          ].val.move(dst)
          return
      gpu_batch_size = self.policy.gpu_batch_size
      num_gpu_batches = self.num_gpu_batches
      num_inner_iterations = self.num_inner_iterations
      left = (
          (b * num_inner_iterations + t) * num_gpu_batches + k
      ) * gpu_batch_size
      right = left + gpu_batch_size
      if i == 0:  # load from the input ids
          val = dst.allocate(
              (gpu_batch_size, self.task.prompt_len), np.int64
          )
          val.load_from_np(
              self.output_ids[left:right, : self.task.prompt_len]
          )
      else:  # load from the last generated token
          pos = self.task.prompt_len + i
          val = dst.allocate((gpu_batch_size, 1), np.int64)
          val.load_from_np(self.output_ids[left:right, pos - 1 : pos])
      self.hidden[t][i][j][k].store(val)
  
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 7 for calling the DistOptLM obj's underlying member method code with example usage: obj.load_hidden(b, t, i, j, k) without return types