docstr: TwoLayerLinearModel has two Linear modules but we only quantize the second one
  `fc2`, and `fc1`is not quantized
docstr_tok: ["TwoLayerLinearModel","has","two","Linear","modules","but","we","only","quantize","the","second","one","`","fc2","`",",","and","`","fc1","`","is","not","quantized"]
code: def test_two_layers(self):
      r"""TwoLayerLinearModel has two Linear modules but we only quantize the second one
      `fc2`, and `fc1`is not quantized
      """
      for dtype in [torch.qint8, torch.float16]:
          model = TwoLayerLinearModel().eval()
          qconfig = (
              float16_dynamic_qconfig
              if dtype == torch.float16
              else default_dynamic_qconfig
          )
          qconfig_dict = {"fc2": qconfig}
          prepare_dynamic(model, qconfig_dict)
  
          convert_dynamic(model)
  
          def checkQuantized(model):
              self.assertEqual(type(model.fc1), torch.nn.Linear)
              self.checkDynamicQuantizedLinear(model.fc2, dtype=dtype)
              self.checkScriptable(
                  model, self.calib_data, check_save_load=True
              )
              self.checkNoQconfig(model)
  
          checkQuantized(model)
  
          # test one line API
          model = quantize_dynamic(
              TwoLayerLinearModel().eval(), qconfig_dict
          )
          checkQuantized(model)
  
          # Test set API
          model = quantize_dynamic(
              TwoLayerLinearModel().eval(), {"fc2"}, 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 2 for calling the TestQuantizeEagerPTQDynamic obj's underlying member method code with example usage: obj.test_two_layers() without return types