                     "displayed.")))
          argument_list.append(dict(
              opts=("-f", "--freeze"),
              action="store_true",
              dest="freeze",
              default=False,
              group=_("model"),
              help=_("Freeze the weights of the model. Freezing weights means that some of the "
                     "parameters in the model will no longer continue to learn, but those that are "
                     "not frozen will continue to learn. For most models, this will freeze the "
 
</s> remove             opts=("-f", "--freeze"),
 </s> add             opts=("-f", "--freeze-weights"), </s> remove         if not self._args.freeze:
 </s> add         if not self._args.freeze_weights: </s> remove         # Standardized config for freezing weights of other layers in model is `freeze-layers`
        to_freeze = self.config.get("freeze_layers")
        to_freeze = to_freeze if to_freeze else ["encoder"]
        for layer in self._model.layers:
            if isinstance(layer, KModel) and layer.name in to_freeze:
 </s> add         to_freeze = self.config.get("freeze_layers")  # Standardized config for freezing weights
        to_freeze = to_freeze if to_freeze else ["encoder"]  # No plugin config
        for layer in self._get_all_sub_models(self._model):
            if layer.name in to_freeze: </s> remove         self._model.summary(print_fn=print_fn)
        for layer in self._model.layers:
            if isinstance(layer, KModel):
                layer.summary(print_fn=print_fn)
 </s> add         for model in self._get_all_sub_models(self._model):
            model.summary(print_fn=print_fn) </s> remove matplotlib==3.0.3
 </s> add matplotlib>3.0.3,<3.3.0
            dest="freeze_weights",
                     "displayed.")))
          argument_list.append(dict(
              opts=("-f", "--freeze"),
              action="store_true",
            dest="freeze_weights",
              default=False,
              group=_("model"),
              help=_("Freeze the weights of the model. Freezing weights means that some of the "
                     "parameters in the model will no longer continue to learn, but those that are "
                     "not frozen will continue to learn. For most models, this will freeze the "
keep keep keep keep replace keep keep keep keep keep
 Fixups
  - Documentation - Update sphinx_requirements.txt
  - plugins.train.model - Add recursive keras model scanning
  - Change "freeze" option to "freeze-weights" 
lib/cli/args.py