code: def forward(
      self,
      hidden_states: Optional[Tuple[torch.FloatTensor]],
      layer_past: Optional[Tuple[torch.Tensor]] = None,
      attention_mask: Optional[torch.FloatTensor] = None,
      head_mask: Optional[torch.FloatTensor] = None,
      encoder_hidden_states: Optional[torch.Tensor] = None,
      encoder_attention_mask: Optional[torch.FloatTensor] = None,
      use_cache: Optional[bool] = False,
      output_attentions: Optional[bool] = False,
  ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]:
      if encoder_hidden_states is not None:
          if not hasattr(self, "q_attn"):
              raise ValueError(
                  "If class is used as cross attention, the weights `q_attn` have to be defined. "
                  "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`."
              )
  
          query = self.q_attn(hidden_states)
          key, value = self.c_attn(encoder_hidden_states).split(
              self.split_size, dim=2
          )
          attention_mask = encoder_attention_mask
      else:
          query, key, value = self.c_attn(hidden_states).split(
              self.split_size, dim=2
          )
  
      query = self._split_heads(query, self.num_heads, self.head_dim)
      key = self._split_heads(key, self.num_heads, self.head_dim)
      value = self._split_heads(value, self.num_heads, self.head_dim)
  
      if layer_past is not None:
          past_key, past_value = layer_past
          key = torch.cat((past_key, key), dim=-2)
          value = torch.cat((past_value, value), dim=-2)
  
      if use_cache is True:
          present = (key, value)
      else:
          present = None
  
      key, value, attention_mask = self.prefix_tuning(
          key, value, hidden_states, attention_mask
      )
      (query,) = adjust_tensors_for_parallel(key, query)
  
      if self.reorder_and_upcast_attn:
          attn_output, attn_weights = self._upcast_and_reordered_attn(
              query, key, value, attention_mask, head_mask
          )
      else:
          attn_output, attn_weights = self._attn(
              query, key, value, attention_mask, head_mask
          )
  
      attn_output = self._merge_heads(
          attn_output, self.num_heads, self.head_dim
      )
      attn_output = self.c_proj(attn_output)
      attn_output = self.resid_dropout(attn_output)
  
      outputs = (attn_output, present)
      if output_attentions:
          outputs += (attn_weights,)
  
      return outputs
  
file_path: adapters/src/adapters/models/gpt2/modeling_gpt2.py
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from transformers.models.gpt2.modeling_gpt2 import GPT2Attention, GPT2Block
  from ...composition import adjust_tensors_for_parallel, adjust_tensors_for_parallel_
  from .mixin_gpt2 import GPT2AttentionAdaptersMixin, GPT2DecoderBlockAdaptersMixin
label_desc: Use image node_id 1 for calling the GPT2AttentionWithAdapters obj's underlying member method code with example usage: obj.forward(hidden_states, layer_past, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions) and returns: outputs