code: def forward(
      self,
      input_ids=None,
      attention_mask=None,
      head_mask=None,
      inputs_embeds=None,
      start_positions=None,
      end_positions=None,
      output_attentions=None,
      output_hidden_states=None,
      return_dict=None,
  ):
      if (
          self.training
          or start_positions is not None
          or end_positions is not None
      ):
          raise ValueError(WARN_MSG_FOR_TRAINING_ATTEMPT)
  
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
      if return_dict:
          raise ValueError(WARN_MSG_FOR_DICT_RETURN)
  
      distilbert_output = self.distilbert(
          input_ids=input_ids,
          attention_mask=attention_mask,
          head_mask=head_mask,
          inputs_embeds=inputs_embeds,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=False,
      )
      hidden_states = distilbert_output[
          0
      ]  # (bs, dim, 1, max_query_len)
  
      hidden_states = self.dropout(
          hidden_states
      )  # (bs, dim, 1, max_query_len)
      logits = self.qa_outputs(
          hidden_states
      )  # (bs, 2, 1, max_query_len)
      start_logits, end_logits = logits.split(
          1, dim=1
      )  # (bs, 1, 1, max_query_len) * 2
      start_logits = (
          start_logits.squeeze().contiguous()
      )  # (bs, max_query_len)
      end_logits = (
          end_logits.squeeze().contiguous()
      )  # (bs, max_query_len)
  
      output = (start_logits, end_logits) + distilbert_output[1:]
      total_loss = None
  
      return (
          ((total_loss,) + output) if total_loss is not None else output
      )
  
imports: from ane_transformers.reference.layer_norm import LayerNormANE
  import torch
  import torch.nn
  from transformers.models.distilbert import modeling_distilbert
label_desc: Use image node_id 2 for calling the DistilBertForQuestionAnswering obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, head_mask, inputs_embeds, start_positions, end_positions, output_attentions, output_hidden_states, return_dict) and returns: unknown