docstr: Test post-training dynamic quantization on preserving
  pre forward and post forward hooks of original model
docstr_tok: ["Test","post-training","dynamic","quantization","on","preserving","pre","forward","and","post","forward","hooks","of","original","model"]
code: def test_forward_hooks_preserved(self):
      r"""Test post-training dynamic quantization on preserving
      pre forward and post forward hooks of original model
      """
      for dtype in [torch.qint8, torch.float16]:
          model = SingleLayerLinearDynamicModel().eval()
          qconfig = (
              float16_dynamic_qconfig
              if dtype == torch.float16
              else default_dynamic_qconfig
          )
          qconfig_dict = {"fc1": qconfig}
          convert_dynamic(model)
  
          counter = {
              "pre_forwards": 0,
              "forwards": 0,
          }
  
          def fw_pre_hook(h_module, input):
              counter["pre_forwards"] += 1
  
          def fw_hook(h_module, input, output):
              counter["forwards"] += 1
  
          model.fc1.register_forward_pre_hook(fw_pre_hook)
          model.fc1.register_forward_hook(fw_hook)
          prepare_dynamic(model, qconfig_dict)
  
          def checkHooksIsPresent(model):
              self.assertObjectIn(
                  fw_pre_hook, model.fc1._forward_pre_hooks.values()
              )
              self.assertObjectIn(
                  fw_hook, model.fc1._forward_hooks.values()
              )
              self.assertEqual(
                  len(model.fc1._forward_pre_hooks.values()),
                  1,
                  "Extra pre forward hooks have appeared on a layer",
              )
              self.assertEqual(
                  len(model.fc1._forward_hooks.values()),
                  1,
                  "Extra post forward hooks have appeared on a layer",
              )
  
          checkHooksIsPresent(model)
          test_only_eval_fn(model, self.calib_data)
          convert_dynamic(model)
          checkHooksIsPresent(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 11 for calling the TestQuantizeEagerPTQDynamic obj's underlying member method code with example usage: obj.test_forward_hooks_preserved() without return types