code: def pubdev_5112():
      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
      )
  
      model_id = w2v_model.model_id
      model = h2o.get_model(model_id)
  
      assert (
          model
      ), "Worder2Vec model without a training frame was retrieved"
  
      # Only leading column should be of type String
      leading_column_string_error = False
      try:
          string_frame = h2o.create_frame(
              rows=10, cols=10, real_fraction=1.0, missing_fraction=0.0
          )
          H2OWord2vecEstimator.from_external(external=string_frame)
      except H2OValueError:
          leading_column_string_error = True
  
      assert leading_column_string_error, (
          "Word2Vec pre-trained model should be checked for the leading column"
          " to be string"
      )
      # Other columns should be non-string type
      multiple_string_columns_error = False
      try:
          string_frame = h2o.create_frame(
              rows=10,
              cols=10,
              string_fraction=1.0,
              missing_fraction=0.0,
          )
          H2OWord2vecEstimator.from_external(external=string_frame)
      except H2OValueError:
          multiple_string_columns_error = True
  
      assert multiple_string_columns_error, (
          "Word2Vec pre-trained model should be checked for columns not to have a"
          " String type except for the leading column"
      )
  
imports: import h2o
  from h2o.exceptions import H2OValueError
  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: pubdev_5112() without return types