class_docstr: Word2Vec
class_docstr_tok: ["Word2Vec"]
docstr: Determines vec_size for a pre-trained model after basic model verification.
docstr_tok: ["Determines","vec_size","for","a","pre-trained","model","after","basic","model","verification","."]
code: 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
  
imports: from h2o.estimators.estimator_base import H2OEstimator
  from h2o.exceptions import H2OValueError
  from h2o.frame import H2OFrame
  from h2o.utils.typechecks import assert_is_type, Enum, numeric
label_desc: Use image node_id 27 for calling the H2OWord2vecEstimator obj's underlying member method code with example usage: obj._determine_vec_size(pre_trained) and returns: unknown