code: def parallelize(self, device_map=None):
      warnings.warn(
          "`GPTJForCausalLM.parallelize` is deprecated and will be removed in v5 of Transformers, you should load"
          " your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
          " `device_map` but it needs to be a dictionary module_name to device, so for instance {'transformer.h.0':"
          " 0, 'transformer.h.1': 1, ...}",
          FutureWarning,
      )
      self.device_map = (
          get_device_map(
              len(self.transformer.h), range(torch.cuda.device_count())
          )
          if device_map is None
          else device_map
      )
      assert_device_map(self.device_map, len(self.transformer.h))
      self.transformer.parallelize(self.device_map)
      self.lm_head = self.lm_head.to(self.transformer.first_device)
      self.model_parallel = True
  
imports: import warnings
  from typing import Optional, Tuple, Union
  import torch
  import torch.fx
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, is_torch_fx_proxy, logging
  from ...utils.model_parallel_utils import assert_device_map, get_device_map
  from .configuration_gptj import GPTJConfig
label_desc: Use image node_id 2 for calling the GPTJForCausalLM obj's underlying member method code with example usage: obj.parallelize(device_map) without return types