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: from dataclasses import dataclass
  from typing import Optional, Tuple
  from .configuration_gpt2 import GPT2Config
  from fastNLP.transformers.torch.activations import ACT2FN
  from fastNLP.transformers.torch.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from fastNLP.transformers.torch.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from fastNLP.transformers.torch.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from fastNLP.transformers.torch.utils.model_parallel_utils import assert_device_map, get_device_map
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
  from fastNLP.core.log import logger
label_desc: Use image node_id 3 for calling the GPT2Model obj's underlying member method code with example usage: obj.deparallelize() without return types