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)
  
      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()
          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.Softmax(dim=-1)(attn_weights)
      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 dataclasses import dataclass
  from typing import Optional, Tuple
  from .configuration_gpt2 import GPT2Config
  from fastNLP.transformers.torch.activations import ACT2FN
  from fastNLP.transformers.torch.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from fastNLP.transformers.torch.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from fastNLP.transformers.torch.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from fastNLP.transformers.torch.utils.model_parallel_utils import assert_device_map, get_device_map
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
  from fastNLP.core.log import logger
label_desc: Use image node_id 3 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