code: def _attn(
      self, query, key, value, attention_mask=None, head_mask=None
  ):
      attn_weights = torch.matmul(query, key.transpose(-1, -2))
  
      if self.scale_attn_weights:
          attn_weights = attn_weights / (float(value.size(-1)) ** 0.5)
  
      # Layer-wise attention scaling
      if self.scale_attn_by_inverse_layer_idx:
          attn_weights = attn_weights / float(self.layer_idx + 1)
  
      if not self.is_cross_attention:
          # if only "normal" attention layer implements causal mask
          query_length, key_length = query.size(-2), key.size(-2)
          causal_mask = self.bias[
              :, :, key_length - query_length : key_length, :key_length
          ].bool()
          mask_value = torch.finfo(attn_weights.dtype).min
          # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
          # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
          mask_value = torch.tensor(
              mask_value, dtype=attn_weights.dtype
          ).to(attn_weights.device)
          attn_weights = torch.where(
              causal_mask, attn_weights, mask_value
          )
  
      if attention_mask is not None:
          # Apply the attention mask
          attn_weights = attn_weights + attention_mask
  
      attn_weights = nn.Softmax(dim=-1)(attn_weights)
  
      # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise
      attn_weights = attn_weights.type(value.dtype)
      attn_weights = self.attn_dropout(attn_weights)
  
      # Mask heads if we want to
      if head_mask is not None:
          attn_weights = attn_weights * head_mask
  
      attn_output = torch.matmul(attn_weights, value)
  
      return attn_output, attn_weights
  
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 3 for calling the ImageGPTAttention obj's underlying member method code with example usage: obj._attn(query, key, value, attention_mask, head_mask) and returns: attn_output,  attn_weights