code: def __init__(
      self,
      config,
      is_cross_attention: Optional[bool] = False,
      layer_idx: Optional[int] = None,
  ):
      super().__init__()
  
      max_positions = config.max_position_embeddings
      self.register_buffer(
          "bias",
          torch.tril(
              torch.ones(
                  (max_positions, max_positions), dtype=torch.uint8
              )
          ).view(1, 1, max_positions, max_positions),
      )
      self.register_buffer("masked_bias", torch.tensor(-1e4))
  
      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
  import warnings
  from typing import Any, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.cuda.amp import autocast
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_imagegpt import ImageGPTConfig
label_desc: Use image node_id 1 to create a new ImageGPTAttention object from inherited base classes: nn with example: obj = ImageGPTAttention(config, is_cross_attention, layer_idx)