code: def _attn(
      self,
      query,
      key,
      value,
      attention_mask=None,
      head_mask=None,
  ):
      # compute causal mask from causal mask buffer
      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)
  
      # 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))
  
      attn_weights = attn_weights / self.scale_attn
      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 = 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: from typing import Tuple
  import numpy
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import logging
  from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
  from .configuration_codegen import CodeGenConfig
label_desc: Use image node_id 4 for calling the CodeGenAttention obj's underlying member method code with example usage: obj._attn(query, key, value, attention_mask, head_mask) and returns: attn_output,  attn_weights