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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def nanvl(col1, col2):
 """Returns col1 if it is not NaN, or col2 if col1 is NaN. Both inputs should be floating point columns (:class:`DoubleType` or :class:`FloatType`). [Row(r1=1.0, r2=1.0), Row(r1=2.0, r2=2.0)] """

    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.nanvl(_to_java_column(col1), _to_java_column(col2)))