outputs in the model
  # currently it's not used, but please make sure model(*example_inputs) runs
  example_inputs = (torch.randn(1, 3, 224, 224),)

  # TODO: add backend_config after we split the backend_config for fbgemm and qnnpack
  # e.g. backend_config = get_default_backend_config("fbgemm")
  # `prepare_fx` inserts observers in the model based on qconfig_mapping and
  # backend_config. If the configuration for an operator in qconfig_mapping
  # is supported in the backend_config (meaning it's supported by the target
  # hardware), we'll insert observer modules according to the qconfig_mapping
  # otherwise the configuration in qconfig_mapping will be ignored
  #
  # Example:
  # in qconfig_mapping, user sets linear module to be quantized with quint8 for
  # activation and qint8 for weight:
  # qconfig = torch.ao.quantization.QConfig(
  #     observer=MinMaxObserver.with_args(dtype=torch.quint8),
