code: def __init__(self, config: T5Config):
      super().__init__(config)
      self.model_dim = config.d_model
  
      self.shared = nn.Embedding(config.vocab_size, config.d_model)
  
      encoder_config = deepcopy(config)
      encoder_config.is_decoder = False
      encoder_config.is_encoder_decoder = False
      encoder_config.use_cache = False
      self.encoder = T5Stack(encoder_config, self.shared)
  
      classifier_dropout = (
          config.classifier_dropout
          if hasattr(config, "classifier_dropout")
          else config.dropout_rate
      )
      self.dropout = nn.Dropout(classifier_dropout)
      self.classifier = nn.Linear(config.d_model, config.num_labels)
  
      # Initialize weights and apply final processing
      self.post_init()
  
      # Model parallel
      self.model_parallel = False
      self.device_map = None
  
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 1 to create a new T5ForTokenClassification object from inherited base classes: T5PreTrainedModel with example: obj = T5ForTokenClassification(config)