class_docstr: Word2Vec
class_docstr_tok: ["Word2Vec"]
code: def pre_trained(self, pre_trained):
      pt = self._parms["pre_trained"] = H2OFrame._validate(
          pre_trained, "pre_trained"
      )
      if pt is not None:
          self.vec_size = H2OWord2vecEstimator._determine_vec_size(pt)
  
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 21 for calling the H2OWord2vecEstimator obj's underlying member method code with example usage: obj.pre_trained(pre_trained) without return types