code: def test_dp_attn_hook(
      self,
      batch_size: int,
      src_seq_len: int,
      tgt_seq_len: int,
      num_heads: int,
      bias: bool,
      add_bias_kv: bool,
      add_zero_attn: bool,
      kdim: Optional[int],
      vdim: Optional[int],
  ):
      embed_dim = (
          4 * num_heads
      )  # embed_dim must be divisible by num_heads
  
      dp_attn = DPMultiheadAttention(
          embed_dim,
          num_heads,
          dropout=0.0,  # Untestable between two different implementations
          bias=bias,
          add_bias_kv=add_bias_kv,
          add_zero_attn=add_zero_attn,
          kdim=kdim,
          vdim=vdim,
      )
  
      # Test if the hook function is working within the state_dict
      if "in_proj_bias" in dp_attn.state_dict():
          dp_attn._register_state_dict_hook(remove_in_proj_bias_hook)
          self.assertFalse("in_proj_bias" in dp_attn.state_dict())
  
file_path: opacus/opacus/tests/dp_layers/dp_multihead_attention_test.py
imports: from typing import Optional
  import hypothesis.strategies
  import torch
  import torch.nn
  from hypothesis import given, settings
  from opacus.layers import DPMultiheadAttention
  from .common import DPModules_test
label_desc: Use image node_id 3 for calling the DPMultiheadAttention_test obj's underlying member method code with example usage: obj.test_dp_attn_hook(batch_size, src_seq_len, tgt_seq_len, num_heads, bias, add_bias_kv, add_zero_attn, kdim, vdim) without return types