code: def test_linear_bn_workflow(self):
      qengine = torch.backends.quantized.engine
      m = nn.Sequential(
          QuantStub(),
          nn.Linear(4, 4),
          nn.BatchNorm1d(4),
      )
      data = torch.randn(4, 4)
      m.qconfig = torch.ao.quantization.get_default_qat_qconfig(qengine)
      m = torch.ao.quantization.fuse_modules_qat(m, [["1", "2"]])
      mp = prepare_qat(m)
      mp(data)
      mq = convert(mp)
      self.assertTrue(type(mq[1]) == nnq.Linear)
      self.assertTrue(type(mq[2]) == nn.Identity)
  
imports: import copy
  import math
  import torch
  import torch.nn
  import torch.backends.mkldnn
  from torch.nn import Conv2d, BatchNorm2d, ReLU, init
  from torch.ao.nn.intrinsic.qat import ConvBn2d, ConvBnReLU2d
  from torch.nn.modules.utils import _pair
  import torch.ao.nn.quantized
  import torch.ao.nn.quantized.dynamic
  import torch.ao.nn.qat
  import torch.ao.nn.intrinsic.qat
  import torch.ao.nn.qat.dynamic
  from torch.ao.quantization import prepare, convert, prepare_qat, quantize_qat, QuantStub, DeQuantStub, default_qconfig, default_qat_qconfig, default_embedding_qat_qconfig, default_symmetric_qnnpack_qat_qconfig, get_default_qat_qconfig, FixedQParamsFakeQuantize, FusedMovingAvgObsFakeQuantize, get_embedding_qat_module_mappings, get_embedding_static_quant_module_mappings, NoopObserver
  from torch.ao.quantization.qconfig import qconfig_equals
  from torch.testing._internal.common_quantization import DeFusedEmbeddingBagLinear, QuantizationTestCase, QuantStubModel, ManualLinearQATModel, ManualDropoutQATModel, ManualLinearDynamicQATModel, ManualConvLinearQATModel, ManualConvLinearSymmQATModel, ManualEmbeddingBagLinear, TwoLayerLinearModel, test_only_eval_fn, test_only_train_fn
  from torch.testing._internal.common_quantized import override_quantized_engine, supported_qengines, override_qengines
  from torch.testing._internal.common_utils import skipIfNoXNNPACK
  from hypothesis import given
  from hypothesis import strategies
  import torch.testing._internal.hypothesis_utils
  from functools import reduce
label_desc: Use image node_id 9 for calling the TestQuantizeEagerQATNumerics obj's underlying member method code with example usage: obj.test_linear_bn_workflow() without return types