code: def test_embedding_bag_bfloat16(self, device, dtypes):
      with set_default_dtype(torch.double):
          self._test_EmbeddingBag(
              device,
              "sum",
              True,
              wdtype=torch.bfloat16,
              dtype=dtypes[0],
              odtype=dtypes[1],
              test_backward=True,
          )
          self._test_EmbeddingBag(
              device,
              "mean",
              True,
              wdtype=torch.bfloat16,
              dtype=dtypes[0],
              odtype=dtypes[1],
              test_backward=True,
          )
  
imports: import unittest
  import random
  import itertools
  from itertools import product
  import torch
  from torch.testing._internal.common_utils import run_tests, set_default_dtype, skipIfTorchDynamo, instantiate_parametrized_tests, parametrize, _assertGradAndGradgradChecks, IS_JETSON
  from torch.testing._internal.common_cuda import TEST_CUDA
  from torch.testing._internal.common_nn import NNTestCase
  from torch.testing._internal.common_device_type import onlyNativeDeviceTypes, dtypes, instantiate_device_type_tests, dtypesIfCUDA, onlyCUDA, TEST_WITH_ROCM, skipCUDAIf, skipMeta
  import torch.nn.functional
  import torch.nn
  from torch.testing._internal.common_utils import dtype2prec_DONTUSE
label_desc: Use image node_id 23 for calling the TestEmbeddingNNDeviceType obj's underlying member method code with example usage: obj.test_embedding_bag_bfloat16(device, dtypes) without return types