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:
          assert hasattr(
              self, "q_attn"
          ), "If class is used as cross attention, the weights `q_attn` have to be defined. Please make sure to instantiate class with `Attention(..., 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)
      key = self.split_heads(key, k=True)
      value = self.split_heads(value)
      if layer_past is not None:
          past_key, past_value = (
              layer_past[0].transpose(-2, -1),
              layer_past[1],
          )  # transpose back cf below
          key = torch.cat((past_key, key), dim=-1)
          value = torch.cat((past_value, value), dim=-2)
  
      if use_cache is True:
          present = torch.stack(
              (key.transpose(-2, -1), value)
          )  # transpose to have same shapes for stacking
      else:
          present = (None,)
  
      attn_outputs = self._attn(
          query,
          key,
          value,
          attention_mask,
          head_mask,
          output_attentions,
      )
      a = attn_outputs[0]
  
      a = self.merge_heads(a)
      a = self.c_proj(a)
      a = self.resid_dropout(a)
  
      outputs = [a, present] + attn_outputs[1:]
      return outputs
  
imports: import os
  from dataclasses import dataclass
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
  from transformers.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from transformers.utils import logging
  from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
  from transformers.models.gpt2.configuration_gpt2 import GPT2Config
label_desc: Use image node_id 6 for calling the Attention 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