to configure this
  # example_inputs is a tuple of inputs, that is used to infer the type of the
  # 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_qat_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 fake_quantize modules according to the qconfig_mapping
  # otherwise the configuration in qconfig_mapping will be ignored
  # see :func:`~torch.ao.quantization.prepare_fx` for a detailed explanation of
  # how qconfig_mapping interacts with backend_config
