code: def _attn(
      self, query, key, value, attention_mask=None, head_mask=None
  ):
      # Keep the attention weights computation in fp32 to avoid overflow issues
      query = query.to(torch.float32)
      key = key.to(torch.float32)
  
      attn_weights = torch.matmul(query, key.transpose(-1, -2))
  
      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)
      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.functional.softmax(attn_weights, dim=-1)
      attn_weights = attn_weights.to(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 os
  from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_gpt_neo import GPTNeoConfig
label_desc: Use image node_id 4 for calling the GPTNeoSelfAttention obj's underlying member method code with example usage: obj._attn(query, key, value, attention_mask, head_mask) and returns: attn_output,  attn_weights