code: def _attn(
      self,
      q,
      k,
      v,
      attention_mask=None,
      head_mask=None,
      output_attentions=False,
  ):
      w = torch.matmul(q, k)
      if self.scale:
          w = w / (float(v.size(-1)) ** 0.5)
      nd, ns = w.size(-2), w.size(-1)
  
      if not self.is_cross_attention:
          # if only "normal" attention layer implements causal mask
          mask = self.bias[:, :, ns - nd : ns, :ns]
          w = torch.where(mask.bool(), w, self.masked_bias.to(w.dtype))
  
      if attention_mask is not None:
          # Apply the attention mask
          w = w + attention_mask
  
      w = nn.Softmax(dim=-1)(w)
      w = self.attn_dropout(w)
  
      # Mask heads if we want to
      if head_mask is not None:
          w = w * head_mask
  
      outputs = [torch.matmul(w, v)]
      if output_attentions:
          outputs.append(w)
      return outputs
  
imports: import os
  from dataclasses import dataclass
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
  from transformers.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from transformers.utils import logging
  from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
  from transformers.models.gpt2.configuration_gpt2 import GPT2Config
label_desc: Use image node_id 3 for calling the Attention obj's underlying member method code with example usage: obj._attn(q, k, v, attention_mask, head_mask, output_attentions) and returns: outputs