)
          )
Parameters:
      * float_dict (Dict[str, Any]) -- state dict of
        the float model
  * **quantized_dict** (*Dict**[**str**, **Any**]*) -- state dict
    of the quantized model

Returns:
      dict with key corresponding to module names and each entry being
      a dictionary with two keys 'float' and 'quantized', containing
      the float and quantized weights
Return type:
      weight_dict
torch.ao.ns._numeric_suite.get_logger_dict(mod, prefix='')
Traverse the modules and save all logger stats into target dict.
   This is mainly used for quantization accuracy debug.
Type of loggers supported:
      ShadowLogger: used to log the outputs of the quantized module
      and its matching float shadow module, OutputLogger: used to log
      the outputs of the modules
Parameters:
      * mod (Module) -- module we want to save all logger stats
  * **prefix** (*str*) -- prefix for the current module

Returns: