code: def forward(self, batch, tokenizer, compute_loss: bool = False):
      encoder_output = self.encoder.encode(
          input_ids=batch.input_ids,
          segment_ids=batch.segment_ids,
          input_mask=batch.input_mask,
      )
      logits = self.head(pooled=encoder_output.pooled)
      if compute_loss:
          loss_fct = nn.CrossEntropyLoss()
          loss = loss_fct(
              logits.view(-1, self.head.num_labels),
              batch.label_id.view(-1),
          )
          return LogitsAndLossOutput(
              logits=logits, loss=loss, other=encoder_output.other
          )
      else:
          return LogitsOutput(logits=logits, other=encoder_output.other)
  
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 ClassificationModel obj's underlying member method code with example usage: obj.forward(batch, tokenizer, compute_loss) and returns: LogitsAndLossOutput, LogitsOutput