code: def forward(self, batch, tokenizer, compute_loss: bool = False):
      choice_score_list = []
      encoder_output_other_ls = []
      for i in range(self.num_choices):
          encoder_output = self.encoder.encode(
              input_ids=batch.input_ids[:, i],
              segment_ids=batch.segment_ids[:, i],
              input_mask=batch.input_mask[:, i],
          )
          choice_score = self.head(pooled=encoder_output.pooled)
          choice_score_list.append(choice_score)
          encoder_output_other_ls.append(encoder_output.other)
  
      reshaped_outputs = []
      if encoder_output_other_ls[0]:
          for j in range(len(encoder_output_other_ls[0])):
              reshaped_outputs.append(
                  [
                      torch.stack(
                          [
                              misc[j][layer_i]
                              for misc in encoder_output_other_ls
                          ],
                          dim=1,
                      )
                      for layer_i in range(
                          len(encoder_output_other_ls[0][0])
                      )
                  ]
              )
          reshaped_outputs = tuple(reshaped_outputs)
  
      logits = torch.cat(
          [
              choice_score.unsqueeze(1).squeeze(-1)
              for choice_score in choice_score_list
          ],
          dim=1,
      )
  
      if compute_loss:
          loss_fct = nn.CrossEntropyLoss()
          loss = loss_fct(
              logits.view(-1, self.num_choices), batch.label_id.view(-1)
          )
          return LogitsAndLossOutput(
              logits=logits, loss=loss, other=reshaped_outputs
          )
      else:
          return LogitsOutput(logits=logits, other=reshaped_outputs)
  
imports: import abc
  import torch
  import torch.nn
  from typing import Callable
  import jiant.proj.main.modeling.heads
  from jiant.proj.main.components.outputs import LogitsAndLossOutput
  from jiant.proj.main.components.outputs import LogitsOutput
  from jiant.utils.python.datastructures import take_one
  from jiant.tasks.core import TaskTypes
label_desc: Use image node_id 2 for calling the MultipleChoiceModel obj's underlying member method code with example usage: obj.forward(batch, tokenizer, compute_loss) and returns: LogitsAndLossOutput, LogitsOutput