code: def word2vec_to_frame():
      print("Test converting a word2vec model to a Frame")
  
      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
      )
      word_embeddings = words.cbind(embeddings)
  
      w2v_model = H2OWord2vecEstimator(pre_trained=word_embeddings)
      w2v_model.train()
  
      w2v_frame = w2v_model.to_frame()
  
      word_embeddings.names = w2v_frame.names
      assert word_embeddings.as_data_frame().equals(
          word_embeddings.as_data_frame()
      ), "Source and generated embeddings match"
  
imports: import sys, os
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.word2vec import H2OWord2vecEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: word2vec_to_frame() without return types