code: def class_extensions():
      @staticmethod
      def from_file(file=str, model_id=None):
          """
          Creates new Generic model by loading existing embedded model into library, e.g. from H2O MOJO.
          The imported model must be supported by H2O.
  
          :param file: A string containing path to the file to create the model from
          :param model_id: Model ID
          :return: H2OGenericEstimator instance representing the generic model
  
          :examples:
  
          >>> from h2o.estimators import H2OIsolationForestEstimator, H2OGenericEstimator
          >>> import tempfile
          >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv")
          >>> ifr = H2OIsolationForestEstimator(ntrees=1)
          >>> ifr.train(x=["Origin","Dest"], y="Distance", training_frame=airlines)
          >>> original_model_filename = tempfile.mkdtemp()
          >>> original_model_filename = ifr.download_mojo(original_model_filename)
          >>> model = H2OGenericEstimator.from_file(original_model_filename)
          >>> model.model_performance()
          """
          model = H2OGenericEstimator(path=file, model_id=model_id)
          model.train()
  
          return model
  
label_desc: Use image node_id 1 for calling a global function with example usage: class_extensions() and returns: model