code: def forward(
      self,
      input_ids=None,
      attention_mask=None,
      head_mask=None,
      inputs_embeds=None,
      output_attentions=None,
      output_hidden_states=None,
      return_dict=None,
      head=None,
      output_adapter_gating_scores=False,
      output_adapter_fusion_attentions=False,
      **kwargs,
  ):
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      input_ids = (
          input_ids.view(-1, input_ids.size(-1))
          if input_ids is not None
          else None
      )
      attention_mask = (
          attention_mask.view(-1, attention_mask.size(-1))
          if attention_mask is not None
          else None
      )
      inputs_embeds = (
          inputs_embeds.view(
              -1, inputs_embeds.size(-2), inputs_embeds.size(-1)
          )
          if inputs_embeds is not None
          else None
      )
  
      distilbert_output, context = 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=return_dict,
          output_adapter_gating_scores=output_adapter_gating_scores,
          output_adapter_fusion_attentions=output_adapter_fusion_attentions,
          adapter_input_parallelized=kwargs.pop(
              "adapter_input_parallelized", False
          ),
          output_context=True,
      )
      # required e.g. for prompt tuning in all models
      kwargs["context"] = context
  
      outputs = self.forward_head(
          distilbert_output,
          head_name=head,
          attention_mask=attention_mask,
          return_dict=return_dict,
          **kwargs,
      )
  
      return outputs
  
imports: import torch.nn
  from transformers.models.distilbert.modeling_distilbert import DISTILBERT_INPUTS_DOCSTRING, DISTILBERT_START_DOCSTRING, DistilBertModel, DistilBertPreTrainedModel
  from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward
  from ...heads import BertStyleMaskedLMHead, BiaffineParsingHead, CausalLMHead, ClassificationHead, ModelWithFlexibleHeadsAdaptersMixin, MultiLabelClassificationHead, MultipleChoiceHead, QuestionAnsweringHead, TaggingHead
  from ...model_mixin import EmbeddingAdaptersWrapperMixin
  from ...wrappers import init
label_desc: Use image node_id 4 for calling the DistilBertAdapterModel obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict, head, output_adapter_gating_scores, output_adapter_fusion_attentions) and returns: outputs