docstr: More complicated nested test case with child qconfig overrides
  parent qconfig
docstr_tok: ["More","complicated","nested","test","case","with","child","qconfig","overrides","parent","qconfig"]
code: def test_nested3(self):
      r"""More complicated nested test case with child qconfig overrides
      parent qconfig
      """
      for dtype in [torch.qint8, torch.float16]:
          model = NestedModel().eval()
          qconfig = (
              float16_dynamic_qconfig
              if dtype == torch.float16
              else default_dynamic_qconfig
          )
          qconfig_dynamic_dict = {
              "fc3": qconfig,
              "sub2": qconfig,
              "sub2.fc1": qconfig,
          }
          prepare_dynamic(model, qconfig_dynamic_dict)
  
          convert_dynamic(model)
  
          def checkQuantized(model):
              self.checkDynamicQuantizedLinear(
                  model.sub2.fc1, dtype=dtype
              )
              self.checkDynamicQuantizedLinear(
                  model.sub2.fc2, dtype=dtype
              )
              self.checkDynamicQuantizedLinear(model.fc3, dtype=dtype)
              self.checkScriptable(
                  model, self.calib_data, check_save_load=True
              )
              self.checkNoQconfig(model)
  
          checkQuantized(model)
  
          # test one line API
          model = quantize_dynamic(
              NestedModel().eval(), qconfig_dynamic_dict
          )
          checkQuantized(model)
  
          # Test set API
          model = quantize_dynamic(
              NestedModel().eval(),
              {"fc3", "sub2", "sub2.fc1"},
              dtype=dtype,
          )
          checkQuantized(model)
  
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 5 for calling the TestQuantizeEagerPTQDynamic obj's underlying member method code with example usage: obj.test_nested3() without return types