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 `DecisionTransformerGPT2Attention(..., 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
  
      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
  
imports: import math
  import os
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.cuda.amp import autocast
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
  from ...modeling_utils import PreTrainedModel
  from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
  from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_decision_transformer import DecisionTransformerConfig
label_desc: Use image node_id 7 for calling the DecisionTransformerGPT2Attention 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