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 / (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 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
      ].to(torch.bool)
      attn_weights = torch.where(
          causal_mask,
          attn_weights,
          self.masked_bias.to(attn_weights.dtype),
      )
  
      if attention_mask is not None:
          # Apply the attention mask
          attn_weights = attn_weights + attention_mask
  
      attn_weights = nn.functional.softmax(attn_weights, dim=-1)
  
      # 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: from typing import Optional, Tuple, Union
  import torch
  import torch.nn
  from transformers.activations import ACT2FN
  from transformers.models.gpt2.modeling_gpt2 import BaseModelOutputWithPastAndCrossAttentions, GPT2PreTrainedModel
  from transformers.pytorch_utils import Conv1D
label_desc: Use image node_id 2 for calling the GPT2Attention obj's underlying member method code with example usage: obj._attn(query, key, value, attention_mask, head_mask) and returns: attn_output,  attn_weights