code: def parallelize(self, device_map=None):
      self.device_map = (
          get_device_map(
              len(self.encoder.block), range(torch.cuda.device_count())
          )
          if device_map is None
          else device_map
      )
      assert_device_map(self.device_map, len(self.encoder.block))
      self.encoder.parallelize(self.device_map)
      self.classifier.to(self.encoder.first_device)
      self.model_parallel = True
  
imports: from transformers import T5Config, T5PreTrainedModel
  import torch
  from torch import nn
  from copy import deepcopy
  from typing import Optional, Tuple, Union, List
  from itertools import chain
  from transformers.modeling_outputs import TokenClassifierOutput
  from transformers.models.t5.modeling_t5 import T5Stack
  from transformers.utils.model_parallel_utils import get_device_map, assert_device_map
label_desc: Use image node_id 2 for calling the T5ForTokenClassification obj's underlying member method code with example usage: obj.parallelize(device_map) without return types