code: def parallelize(self, device_map=None):
      # Check validity of device_map
      self.device_map = (
          get_device_map(len(self.h), range(torch.cuda.device_count()))
          if device_map is None
          else device_map
      )
      assert_device_map(self.device_map, len(self.h))
      self.model_parallel = True
      self.first_device = (
          "cpu"
          if "cpu" in self.device_map.keys()
          else "cuda:" + str(min(self.device_map.keys()))
      )
      self.last_device = "cuda:" + str(max(self.device_map.keys()))
      self.wte = self.wte.to(self.first_device)
      self.wpe = self.wpe.to(self.first_device)
      # Load onto devices
      for k, v in self.device_map.items():
          for block in v:
              cuda_device = "cuda:" + str(k)
              self.h[block] = self.h[block].to(cuda_device)
      # ln_f to last
      self.ln_f = self.ln_f.to(self.last_device)
  
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 2 for calling the GPT2Model obj's underlying member method code with example usage: obj.parallelize(device_map) without return types