code: def forward(
      self,
      input_ids=None,
      attention_mask=None,
      token_type_ids=None,
      position_ids=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,
  ):
      input_ids = (
          input_ids.view(-1, input_ids.size(-1))
          if input_ids is not None
          else None
      )
      position_ids = (
          position_ids.view(-1, position_ids.size(-1))
          if position_ids is not None
          else None
      )
      token_type_ids = (
          token_type_ids.view(-1, token_type_ids.size(-1))
          if token_type_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
      )
  
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      outputs, context = self.deberta(
          input_ids,
          attention_mask=attention_mask,
          token_type_ids=token_type_ids,
          position_ids=position_ids,
          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
      # BERT & RoBERTa return the pooled output as second item, we don't need that in these heads
      if not return_dict:
          head_inputs = (outputs[0],) + outputs[2:]
      else:
          head_inputs = outputs
      pooled_output = outputs[0]
  
      if (
          head
          or AdapterSetup.get_context_head_setup()
          or self.active_head
      ):
          head_outputs = self.forward_head(
              head_inputs,
              head_name=head,
              attention_mask=attention_mask,
              return_dict=return_dict,
              pooled_output=pooled_output,
              **kwargs,
          )
          return head_outputs
      else:
          # in case no head is used just return the output of the base model (including pooler output)
          return outputs
  
file_path: adapters/src/adapters/models/deberta_v2/adapter_model.py
imports: from transformers.file_utils import add_start_docstrings
  from transformers.models.deberta_v2.modeling_deberta_v2 import DebertaV2Model, DebertaV2PreTrainedModel
  from ...context import AdapterSetup
  from ...heads import BertStyleMaskedLMHead, ClassificationHead, ModelWithFlexibleHeadsAdaptersMixin, MultiLabelClassificationHead, MultipleChoiceHead, QuestionAnsweringHead, TaggingHead
  from ...model_mixin import EmbeddingAdaptersWrapperMixin
  from ...wrappers import init
label_desc: Use image node_id 2 for calling the DebertaV2AdapterModel obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict, head, output_adapter_gating_scores, output_adapter_fusion_attentions) and returns: head_outputs, outputs