code: def test_dequant_stub(self):
      m = QuantStubModel().eval()
      prepare(m, inplace=True)
      self.checkObservers(m)
      convert(m, inplace=True)
      self.assertEqual(type(m.quant), nnq.Quantize)
      self.assertEqual(type(m.fc), nnq.Linear)
      self.assertEqual(type(m.dequant), nnq.DeQuantize)
  
      # check DeQuantStub is not swapped when it doesn't have a qconfig
      m2 = QuantStubModel().eval()
      m2.dequant.qconfig = None
      prepare(m2, inplace=True)
      self.checkObservers(m2)
      convert(m2, inplace=True)
      self.assertEqual(type(m2.quant), nnq.Quantize)
      self.assertEqual(type(m2.fc), nnq.Linear)
      self.assertEqual(type(m2.dequant), DeQuantStub)
  
imports: import torch
  import torch.nn
  import torch.ao.nn.quantized
  from torch.nn.utils.rnn import PackedSequence
  from torch.ao.quantization import quantize, prepare, convert, prepare_qat, quantize_dynamic, QuantWrapper, QuantStub, DeQuantStub, default_qconfig, default_dynamic_qconfig, per_channel_dynamic_qconfig, float16_dynamic_qconfig, float_qparams_weight_only_qconfig, float_qparams_weight_only_qconfig_4bit, FixedQParamsObserver, PerChannelMinMaxObserver, default_dynamic_quant_observer, default_weight_observer, QConfig
  from torch.testing._internal.common_quantization import QuantizationTestCase, AnnotatedSingleLayerLinearModel, QuantStubModel, ModelWithFunctionals, SingleLayerLinearDynamicModel, TwoLayerLinearModel, NestedModel, ResNetBase, RNNDynamicModel, RNNCellDynamicModel, ActivationsTestModel, NormalizationTestModel, test_only_eval_fn, prepare_dynamic, convert_dynamic, skipIfNoFBGEMM, EmbeddingBagModule, EmbeddingModule, EmbeddingWithStaticLinear, LinearReluLinearModel
  from torch.testing._internal.common_quantization import AnnotatedTwoLayerLinearModel, AnnotatedNestedModel, AnnotatedSubNestedModel, AnnotatedCustomConfigNestedModel, AnnotatedSkipQuantModel
  from torch.testing._internal.common_quantized import override_quantized_engine, supported_qengines, override_qengines
  from hypothesis import given
  from hypothesis import strategies
  import torch.testing._internal.hypothesis_utils
  from typing import Tuple
  import numpy
label_desc: Use image node_id 14 for calling the TestQuantizeEagerPTQStatic obj's underlying member method code with example usage: obj.test_dequant_stub() without return types