Prepare the model by attaching the logger to both float module and
   quantized module if they are in the allow_list.
Parameters:
      * float_module (Module) -- float module used to generate
        the q_module
  * **q_module** (*Module*) -- module quantized from float_module

  * **logger_cls** -- type of logger to be attached to
    float_module and q_module

  * **allow_list** -- list of module types to attach logger

torch.ao.ns._numeric_suite.compare_model_outputs(float_model, q_model, *data, logger_cls=, allow_list=None)
Compare output activations between float and quantized models at
   corresponding locations for the same input. Return a dict with key
   corresponding to quantized module names and each entry being a
   dictionary with two keys 'float' and 'quantized', containing the
   activations of quantized model and float model at matching
   locations. This dict can be used to compare and compute the