class_docstr: The order or base classes is important here as we don't want to inherit the instance properties of ModelBase.
class_docstr_tok: ["The","order","or","base","classes","is","important","here","as","we","do","n't","want","to","inherit","the","instance","properties","of","ModelBase","."]
docstr: Transform the given H2OFrame using the fitted model.
  
  :param H2OFrame X: May contain NAs and/or categorical data.
  :param H2OFrame y: Ignored by transformers. Should be None.
  :param params: Ignored.
  
  :returns: The transformed H2OFrame.
docstr_tok: ["Transform","the","given","H2OFrame","using","the","fitted","model",".",":","param","H2OFrame","X",":","May","contain","NAs","and\/or","categorical","data",".",":","param","H2OFrame","y",":","Ignored","by","transformers",".","Should","be","None",".",":","param","params",":","Ignored",".",":","returns",":","The","transformed","H2OFrame","."]
code: def transform(self, X, y=None, **params):
      """
      Transform the given H2OFrame using the fitted model.
  
      :param H2OFrame X: May contain NAs and/or categorical data.
      :param H2OFrame y: Ignored by transformers. Should be None.
      :param params: Ignored.
  
      :returns: The transformed H2OFrame.
      """
      return self._delegate.predict(X)
  
file_path: h2o-3/h2o-py/h2o/transforms/decomposition.py
imports: from h2o.frame import H2OFrame
  from ..estimators import H2OPrincipalComponentAnalysisEstimator, H2OSingularValueDecompositionEstimator
  from ..model import ModelBase
  from .transform_base import H2OTransformer
label_desc: Use image node_id 7 for calling the _H2OTransformerProxy obj's underlying member method code with example usage: obj.transform(X, y) and returns: self