code: def __init__(self, config, is_cross_attention=False, layer_idx=None):
      super().__init__()
  
      max_positions = config.max_position_embeddings
      self.register_buffer(
          "bias",
          torch.tril(
              torch.ones(
                  (max_positions, max_positions), dtype=torch.bool
              )
          ).view(1, 1, max_positions, max_positions),
          persistent=False,
      )
      self.register_buffer(
          "masked_bias", torch.tensor(-1e4), persistent=False
      )
  
      self.embed_dim = config.hidden_size
      self.num_heads = config.num_attention_heads
      self.head_dim = self.embed_dim // self.num_heads
      self.split_size = self.embed_dim
      if self.head_dim * self.num_heads != self.embed_dim:
          raise ValueError(
              f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
              f" {self.num_heads})."
          )
  
      self.scale_attn_weights = config.scale_attn_weights
      self.is_cross_attention = is_cross_attention
  
      # Layer-wise attention scaling, reordering, and upcasting
      self.scale_attn_by_inverse_layer_idx = (
          config.scale_attn_by_inverse_layer_idx
      )
      self.layer_idx = layer_idx
      self.reorder_and_upcast_attn = config.reorder_and_upcast_attn
  
      if self.is_cross_attention:
          self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim)
          self.q_attn = Conv1D(self.embed_dim, self.embed_dim)
      else:
          self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim)
      self.c_proj = Conv1D(self.embed_dim, self.embed_dim)
  
      self.attn_dropout = nn.Dropout(config.attn_pdrop)
      self.resid_dropout = nn.Dropout(config.resid_pdrop)
  
      self.pruned_heads = set()
  
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 1 to create a new DecisionTransformerGPT2Attention object from inherited base classes: nn with example: obj = DecisionTransformerGPT2Attention(config, is_cross_attention, layer_idx)