code: def test_multihead_attention_dtype_batch_first(self, device, dtype):
      embed_dim = 128
      num_heads = 8
      sl = 10
      bs = 8
      # With batch_first=True, we have the possibility of hitting
      # the native fast path if we call .eval() and enable inference
      # mode. Test both paths.
      for training in (True, False):
          model = (
              nn.MultiheadAttention(
                  embed_dim, num_heads, batch_first=True
              )
              .to(device)
              .to(dtype)
          )
          if not training:
              model = model.eval()
              cm = torch.no_grad()
          else:
              cm = contextlib.nullcontext()
          with cm:
              q = torch.randn(
                  bs, sl, embed_dim, device=device, dtype=dtype
              )
              k = torch.randn(
                  bs, sl, embed_dim, device=device, dtype=dtype
              )
              v = torch.randn(
                  bs, sl, embed_dim, device=device, dtype=dtype
              )
              # fast path currently doesn't support weights
              out = model(q, k, v, need_weights=False)
              self.assertEqual(q.size(), out[0].size())
              self.assertEqual(dtype, out[0].dtype)
  
imports: import contextlib
  import random
  import unittest
  import unittest.mock
  from torch.nn import MultiheadAttention
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes, onlyCUDAAndPRIVATEUSE1
  from torch.testing._internal.common_nn import NNTestCase
  from torch.testing._internal.common_utils import run_tests, TEST_NUMPY, TEST_WITH_CROSSREF, parametrize, instantiate_parametrized_tests
  import torch.nn
  import torch
label_desc: Use image node_id 4 for calling the TestMultiheadAttentionNNDeviceType obj's underlying member method code with example usage: obj.test_multihead_attention_dtype_batch_first(device, dtype) without return types