code: def deparallelize(self):
      self.model_parallel = False
      self.device_map = None
      self.first_device = "cpu"
      self.last_device = "cpu"
      self.wte = self.wte.to("cpu")
      self.wpe = self.wpe.to("cpu")
      for index in range(len(self.h)):
          self.h[index] = self.h[index].to("cpu")
      self.ln_f = self.ln_f.to("cpu")
      torch.cuda.empty_cache()
  
imports: import os
  from dataclasses import dataclass
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
  from transformers.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from transformers.utils import logging
  from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
  from transformers.models.gpt2.configuration_gpt2 import GPT2Config
label_desc: Use image node_id 4 for calling the GPT2Model obj's underlying member method code with example usage: obj.deparallelize() without return types