code: def test_multihead_attention(
      self,
      N: int,
      T: int,
      D: int,
      P: int,
      bias: bool,
      add_bias_kv: bool,
      add_zero_attn: bool,
      kv_dim: bool,
      test_or_check: int,
  ):
      if kv_dim:
          kdim, vdim = D, D
      else:
          kdim, vdim = None, None
      attn = DPMultiheadAttentionAdapter(
          D,
          P,
          bias=bias,
          add_bias_kv=add_bias_kv,
          add_zero_attn=add_zero_attn,
          dropout=0.0,
          kdim=kdim,
          vdim=vdim,
      )
      q = torch.randn([T, N, D])
      k = torch.randn([T, N, D])
      v = torch.randn([T, N, D])
      x = torch.stack((q, k, v), dim=-1)
  
      self.run_test(x, attn, batch_first=False)
  
file_path: opacus/opacus/tests/grad_samples/dp_multihead_attention_test.py
imports: import hypothesis.strategies
  import torch
  import torch.nn
  from hypothesis import given, settings
  from opacus.layers import DPMultiheadAttention
  from .common import GradSampleHooks_test
label_desc: Use image node_id 1 for calling the MultiHeadAttention_test obj's underlying member method code with example usage: obj.test_multihead_attention(N, T, D, P, bias, add_bias_kv, add_zero_attn, kv_dim, test_or_check) without return types