code: def forward(
      self,
      hidden_states: Optional[Tuple[torch.FloatTensor]],
      attention_mask: Optional[torch.FloatTensor] = None,
      head_mask: Optional[torch.FloatTensor] = None,
  ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]:
      # query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)
      qkv = self.c_attn(hidden_states)
  
      # 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)
      query, key, value = self._split_heads(
          qkv, self.num_heads, 3 * self.head_dim
      ).split(self.head_dim, dim=3)
      (key, value)
  
      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)
      return attn_output
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.nn
  from transformers.activations import ACT2FN
  from transformers.models.gpt2.modeling_gpt2 import BaseModelOutputWithPastAndCrossAttentions, GPT2PreTrainedModel
  from transformers.pytorch_utils import Conv1D
label_desc: Use image node_id 5 for calling the GPT2Attention obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, head_mask) and returns: attn_output