code: def forward(
      self,
      input_ids: Optional[torch.LongTensor] = None,
      attention_mask: Optional[torch.FloatTensor] = None,
      head_mask: Optional[torch.FloatTensor] = None,
      inputs_embeds: Optional[torch.FloatTensor] = None,
      labels: Optional[torch.LongTensor] = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
  ) -> Union[Tuple[torch.FloatTensor], TokenClassifierOutput]:
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      outputs = self.encoder(
          input_ids=input_ids,
          attention_mask=attention_mask,
          inputs_embeds=inputs_embeds,
          head_mask=head_mask,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
      )
  
      sequence_output = outputs[0]
  
      sequence_output = self.dropout(sequence_output)
      logits = self.classifier(sequence_output)
  
      loss = None
  
      if not return_dict:
          output = (logits,) + outputs[2:]
          return ((loss,) + output) if loss is not None else output
  
      return TokenClassifierOutput(
          loss=loss,
          logits=logits,
          hidden_states=outputs.hidden_states,
          attentions=outputs.attentions,
      )
  
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 8 for calling the T5ForTokenClassification obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict) and returns: TokenClassifierOutput, unknown