code: def test_dp_attn(
      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
  
      attn = nn.MultiheadAttention(
          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,
      )
      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,
      )
  
      attn.load_state_dict(dp_attn.state_dict())
  
      q = torch.randn(tgt_seq_len, batch_size, embed_dim)
      k = torch.randn(
          src_seq_len,
          batch_size,
          kdim if kdim is not None else embed_dim,
      )
      v = torch.randn(
          src_seq_len,
          batch_size,
          vdim if vdim is not None else embed_dim,
      )
  
      self.compare_forward_outputs(
          attn,
          dp_attn,
          q,
          k,
          v,
          output_names=("attn_out", "attn_out_weights"),
          atol=1e-5,
          rtol=1e-3,
          key_padding_mask=None,
          need_weights=True,
          attn_mask=None,
      )
  
      self.compare_gradients(
          attn,
          dp_attn,
          attn_train_fn,
          q,
          k,
          v,
          atol=1e-5,
          rtol=1e-3,
          key_padding_mask=None,
          need_weights=True,
          attn_mask=None,
      )
  
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 2 for calling the DPMultiheadAttention_test obj's underlying member method code with example usage: obj.test_dp_attn(batch_size, src_seq_len, tgt_seq_len, num_heads, bias, add_bias_kv, add_zero_attn, kdim, vdim) without return types