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(unpooled=encoder_output.unpooled)
      # Ensure logits in valid range is at least self.offset_margin higher than others
      logits_offset = logits.max() - logits.min() + self.offset_margin
      logits = (
          logits
          + logits_offset * batch.selection_token_mask.unsqueeze(dim=2)
      )
      if compute_loss:
          loss_fct = nn.CrossEntropyLoss()
          loss = loss_fct(
              logits.transpose(dim0=1, dim1=2).flatten(end_dim=1),
              batch.gt_span_idxs.flatten(),
          )
          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 SpanPredictionModel obj's underlying member method code with example usage: obj.forward(batch, tokenizer, compute_loss) and returns: LogitsAndLossOutput, LogitsOutput