code: def forward(
      self,
      input_ids=None,
      past_key_values=None,
      attention_mask=None,
      token_type_ids=None,
      position_ids=None,
      head_mask=None,
      inputs_embeds=None,
      encoder_hidden_states=None,
      encoder_attention_mask=None,
      use_cache=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
      )
  
      outputs, context = self.transformer(
          input_ids,
          past_key_values=past_key_values,
          attention_mask=attention_mask,
          token_type_ids=token_type_ids,
          position_ids=position_ids,
          head_mask=head_mask,
          inputs_embeds=inputs_embeds,
          encoder_hidden_states=encoder_hidden_states,
          encoder_attention_mask=encoder_attention_mask,
          use_cache=use_cache,
          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
  
      batch_size = outputs[0].shape[0]
  
      if self.config.pad_token_id is None:
          # TODO-AH: this may result in unexpected behavior for classification. Find a better way to do this?
          sequence_lengths = -1
      else:
          if input_ids is not None:
              sequence_lengths = (
                  torch.ne(input_ids, self.config.pad_token_id).sum(-1)
                  - 1
              )
              (sequence_lengths,) = adjust_tensors_for_parallel(
                  outputs[0], sequence_lengths
              )
          else:
              sequence_lengths = -1
              logger.warning(
                  f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
                  "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
              )
  
      cls_logits = outputs[0][range(batch_size), sequence_lengths]
  
      outputs = self.forward_head(
          outputs,
          head_name=head,
          cls_output=cls_logits,
          attention_mask=attention_mask,
          return_dict=return_dict,
          **kwargs,
      )
  
      return outputs
  
file_path: adapters/src/adapters/models/gpt2/adapter_model.py
imports: import logging
  import torch
  from adapters.heads.base import QuestionAnsweringHead
  from transformers.models.gpt2.modeling_gpt2 import GPT2_START_DOCSTRING, GPT2Model, GPT2PreTrainedModel
  from transformers.utils import add_start_docstrings
  from ...composition import adjust_tensors_for_parallel
  from ...heads import CausalLMHead, ClassificationHead, ModelWithFlexibleHeadsAdaptersMixin, MultiLabelClassificationHead, TaggingHead
  from ...model_mixin import EmbeddingAdaptersWrapperMixin
  from ...wrappers import init
label_desc: Use image node_id 2 for calling the GPT2AdapterModel obj's underlying member method code with example usage: obj.forward(input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, output_hidden_states, return_dict, head, output_adapter_gating_scores, output_adapter_fusion_attentions) and returns: outputs