code: def test_int16(self):
      class Int16ActQuantizer(Quantizer):
          def annotate(
              self, model: torch.fx.GraphModule
          ) -> torch.fx.GraphModule:
              # using int32 to simulate int16
              int16_qspec = QuantizationSpec(
                  dtype=torch.int16,
                  quant_min=-(2**15),
                  quant_max=2**15 - 1,
                  qscheme=torch.per_tensor_affine,
                  is_dynamic=False,
                  observer_or_fake_quant_ctr=observer.default_observer,
              )
              int8_qspec = QuantizationSpec(
                  dtype=torch.int8,
                  quant_min=-128,
                  quant_max=127,
                  qscheme=torch.per_tensor_symmetric,
                  is_dynamic=False,
                  observer_or_fake_quant_ctr=observer.default_weight_observer,
              )
              quantization_config = QuantizationConfig(
                  input_activation=int16_qspec,
                  weight=int8_qspec,
                  bias=None,
                  output_activation=int16_qspec,
              )
              OP_TO_ANNOTATOR["conv"](model, quantization_config)
  
          def validate(self, model: torch.fx.GraphModule) -> None:
              pass
  
      class M(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.conv = torch.nn.Conv2d(3, 3, 3)
  
          def forward(self, x):
              return self.conv(x)
  
      quantizer = Int16ActQuantizer()
      node_occurrence = {
          # one for input of the first conv, one for output for the first conv
          torch.ops.quantized_decomposed.quantize_per_tensor.default: 2,
          torch.ops.quantized_decomposed.dequantize_per_tensor.default: 3,
      }
      node_list = [
          torch.ops.quantized_decomposed.dequantize_per_tensor.default,
          torch.ops.quantized_decomposed.dequantize_per_tensor.default,
          torch.ops.aten.conv2d.default,
          torch.ops.quantized_decomposed.quantize_per_tensor.default,
      ]
      example_inputs = (torch.randn(1, 3, 3, 3),)
      self._test_quantizer(
          M().eval(),
          example_inputs,
          Int16ActQuantizer(),
          node_occurrence,
          node_list,
      )
  
imports: from typing import List, Tuple
  import torch
  from torch._export import capture_pre_autograd_graph
  from torch import Tensor
  from torch.ao.quantization import observer, ObserverOrFakeQuantize, QConfigMapping
  from torch.ao.quantization.quantizer import DerivedQuantizationSpec, FixedQParamsQuantizationSpec, QuantizationAnnotation, QuantizationSpec, Quantizer, SharedQuantizationSpec
  from torch.ao.quantization.quantizer.xnnpack_quantizer import XNNPACKQuantizer, get_symmetric_quantization_config
  from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import OP_TO_ANNOTATOR, QuantizationConfig
  from torch.ao.quantization.quantizer.composable_quantizer import ComposableQuantizer
  from torch.ao.quantization.quantizer.embedding_quantizer import EmbeddingQuantizer
  from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e, prepare_qat_pt2e
  from torch.ao.quantization.qconfig import default_per_channel_symmetric_qnnpack_qconfig, float_qparams_weight_only_qconfig, per_channel_weight_observer_range_neg_127_to_127, QConfig, weight_observer_range_neg_127_to_127
  from torch.fx import Node
  from torch.testing._internal.common_quantization import NodeSpec, PT2EQuantizationTestCase, skipIfNoQNNPACK, TestHelperModules
  from torch.testing._internal.common_utils import TemporaryFileName
label_desc: Use image node_id 12 for calling the TestQuantizePT2E obj's underlying member method code with example usage: obj.test_int16() and returns: self