code: def word2vec_export():
      print("###### WORD2VEC ######")
      words = h2o.create_frame(
          rows=1000, cols=1, string_fraction=1.0, missing_fraction=0.0
      )
      embeddings = h2o.create_frame(
          rows=1000, cols=100, real_fraction=1.0, missing_fraction=0.0
      )
      frame = words.cbind(embeddings)
      model = H2OWord2vecEstimator(pre_trained=frame)
      model.train()
      expect_error(model.download_pojo, model="Word2Vec", format="POJO")
      model.download_mojo(path=RESULT_DIR)
  
imports: import sys
  import random
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.deeplearning import H2ODeepLearningEstimator
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  from h2o.estimators.glrm import H2OGeneralizedLowRankEstimator
  from h2o.estimators.kmeans import H2OKMeansEstimator
  from h2o.estimators.naive_bayes import H2ONaiveBayesEstimator
  from h2o.estimators.pca import H2OPrincipalComponentAnalysisEstimator
  from h2o.estimators.random_forest import H2ORandomForestEstimator
  from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
  from h2o.estimators.word2vec import H2OWord2vecEstimator
  from h2o.exceptions import H2OValueError
label_desc: Use image node_id 11 for calling a global function with example usage: word2vec_export() without return types