code: def forward(
      self,
      hidden_states,
      layer_past=None,
      attention_mask=None,
      head_mask=None,
      encoder_hidden_states=None,
      encoder_attention_mask=None,
      use_cache=False,
      output_attentions=False,
  ):
      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
  
      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
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple
  from .configuration_gpt2 import GPT2Config
  from fastNLP.transformers.torch.activations import ACT2FN
  from fastNLP.transformers.torch.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from fastNLP.transformers.torch.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from fastNLP.transformers.torch.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from fastNLP.transformers.torch.utils.model_parallel_utils import assert_device_map, get_device_map
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
  from fastNLP.core.log import logger
label_desc: Use image node_id 6 for calling the GPT2Attention 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