code: def test_int16_reference_module(self):
      class RefM(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.conv = nn.ConvTranspose2d(1, 1, 1)
              self.quant1 = QuantStub()
              self.dequant1 = DeQuantStub()
              self.quant2 = QuantStub()
              self.dequant2 = DeQuantStub()
  
          def forward(self, x):
              x = self.quant1(x)
              x = self.dequant1(x)
              x = self.conv(x)
              x = self.quant2(x)
              x = self.dequant2(x)
              return x
  
      input_size = (16, 1, 10, 10)
      data = torch.randn(*input_size, dtype=torch.float)
  
      original_ref_m = RefM()
      rand_w = torch.randn_like(original_ref_m.conv.weight)
      rand_b = torch.randn_like(original_ref_m.conv.bias)
      original_ref_m.conv.weight = torch.nn.Parameter(
          rand_w, requires_grad=False
      )
      original_ref_m.conv.bias = torch.nn.Parameter(
          rand_b, requires_grad=False
      )
  
      qengine = torch.backends.quantized.engine
      if qengine not in supported_qengines:
          return
      from torch.ao.quantization.observer import (
          MovingAverageMinMaxObserver,
      )
  
      weight_obs = MovingAverageMinMaxObserver.with_args(
          dtype=torch.qint32,
          # set qmin and qmax to represent qint16
          quant_min=-1 * (2**15),
          quant_max=(2**15) - 1,
          qscheme=torch.per_tensor_symmetric,
      )
      act_obs = MovingAverageMinMaxObserver.with_args(
          dtype=torch.qint32,
          quant_min=-1 * (2**15),
          quant_max=(2**15) - 1,
      )
      custom_qconfig = QConfig(activation=act_obs, weight=weight_obs)
  
      # quantize the reference model
      original_ref_m.eval()
      original_ref_m.qconfig = custom_qconfig
  
      ref_m = prepare(original_ref_m)
      # calibration
      ref_m(torch.randn(*input_size, dtype=torch.float))
  
      ref_m = convert(ref_m, is_reference=True)
  
      myobs = MovingAverageMinMaxObserver(
          averaging_constant=0.5,
          dtype=torch.qint32,
          # set qmin and qmax to represent qint16
          quant_min=-1 * (2**15),
          quant_max=(2**15) - 1,
          qscheme=torch.per_tensor_symmetric,
      )
      result = myobs(rand_w)
      qparams = myobs.calculate_qparams()
      self.assertEqual(ref_m.conv.weight_scale, qparams[0])
  
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 9 for calling the TestQuantizeEagerOps obj's underlying member method code with example usage: obj.test_int16_reference_module() and returns: x