def load(self, fname: str = None) -> None:
if fname is None:<EOL><INDENT>fname = self.load_path<EOL><DEDENT>fname = Path(fname).with_suffix('<STR_LIT>')<EOL>if fname.exists():<EOL><INDENT>log.info("<STR_LIT>".format(self.model_class, str(fname)))<EOL>with open(fname, "<STR_LIT:rb>") as f:<EOL><INDENT>self.model = pickle.load(f)<EOL><DEDENT>warm_start = self.model_params.get("<STR_LIT>", None)<EOL>self.model_params = {param: getattr(self.model, param) for param in self.get_class_attributes(self.model)}<EOL>self.model_class = self.model.__module__ + self.model.__class__.__name__<EOL>log.info("<STR_LIT>".format(self.model_class))<EOL>if warm_start and "<STR_LIT>" in self.model_params.keys():<EOL><INDENT>self.model_params["<STR_LIT>"] = True<EOL>log.info("<STR_LIT>")<EOL><DEDENT>else:<EOL><INDENT>log.warning("<STR_LIT>"<EOL>"<STR_LIT>")<EOL><DEDENT><DEDENT>else:<EOL><INDENT>log.warning("<STR_LIT>".format(str(fname)))<EOL>self.init_from_scratch()<EOL><DEDENT>return<EOL>
Initialize ``self.model`` as some sklearn model from saved re-initializing ``self.model_params`` parameters. \
    If in new given parameters ``warm_start`` is set to True and given model admits ``warm_start`` parameter, \
    model will be initilized from saved with opportunity to continue fitting.

Args:
    fname: string name of path to model to load from

Returns:
    None