          logger.debug("plugin name: %s, plugins: %s, test result: %s, retval: %s",
                       self._plugin.name, plugins, test, retval)
          return retval
  
      def _load(self) -> keras.models.Model:
          """ Loads the model from disk
  
          If the predict function is to be called and the model cannot be found in the model folder
          then an error is logged and the process exits.
  
 
</s> remove         A list of all :class:`keras.models.Model` objects found within the given model. The
        provided model will always be returned in the first position
 </s> add         A list of all :class:`tensorflow.keras.models.Model` objects found within the given model.
        The provided model will always be returned in the first position </s> remove         retval = get_all_sub_models(load_model(self._weights_file, compile=False))
 </s> add         retval = get_all_sub_models(kmodels.load_model(self._weights_file, compile=False)) </s> remove     def get_config(self):
 </s> add     def get_config(self) -> dict[str, T.Any]:
    def _load(self) -> tf.keras.models.Model:
          logger.debug("plugin name: %s, plugins: %s, test result: %s, retval: %s",
                       self._plugin.name, plugins, test, retval)
          return retval
  
    def _load(self) -> tf.keras.models.Model:
          """ Loads the model from disk
  
          If the predict function is to be called and the model cannot be found in the model folder
          then an error is logged and the process exits.
  
keep keep keep keep replace keep keep keep keep keep
 Training fixes:
  - Fix mixed precision layer storing and switching
  - typofix in _requirements_base.txt
  - lib.model.layers - cleanup, docs and tests fixes
  - lib.model.nn_blocks - typing fixes 
plugins/train/model/_base/io.py