code: def class_extensions():
      @staticmethod
      def from_external(external=H2OFrame):
          """
          Creates new H2OWord2vecEstimator based on an external model.
  
          :param external: H2OFrame with an external model
          :return: H2OWord2vecEstimator instance representing the external model
  
          :examples:
  
          >>> words = h2o.create_frame(rows=10, cols=1,
          ...                          string_fraction=1.0,
          ...                          missing_fraction=0.0)
          >>> embeddings = h2o.create_frame(rows=10, cols=100,
          ...                               real_fraction=1.0,
          ...                               missing_fraction=0.0)
          >>> word_embeddings = words.cbind(embeddings)
          >>> w2v_model = H2OWord2vecEstimator.from_external(external=word_embeddings)
          """
          w2v_model = H2OWord2vecEstimator(pre_trained=external)
          w2v_model.train()
          return w2v_model
  
      @staticmethod
      def _determine_vec_size(pre_trained):
          """
          Determines vec_size for a pre-trained model after basic model verification.
          """
          first_column = pre_trained.types[pre_trained.columns[0]]
  
          if first_column != "string":
              raise H2OValueError(
                  "First column of given pre_trained model %s is required to be a String",
                  pre_trained.frame_id,
              )
  
          if list(pre_trained.types.values()).count("string") > 1:
              raise H2OValueError(
                  "There are multiple columns in given pre_trained model %s with a String type.",
                  pre_trained.frame_id,
              )
  
          return pre_trained.dim[1] - 1
  
label_desc: Use image node_id 1 for calling a global function with example usage: class_extensions() and returns: w2v_model, unknown