def aggregate_spark(data, input):
    """Aggregate a column in a PySpark context.
​
    This function is required.
​
    Args:
        data: A dataframe including all of the raw columns.
​
        input: The aggregate's input object. Column references in the input are
            replaced by their names (e.g. "@column1" will be replaced with "column1"),
            and all other resource references (e.g. constants) are replaced by their
            runtime values.
​
    Returns:
        Any serializable object that matches the output type of the aggregator.
    """
    pass

def aggregate_spark(data, input):
    from pyspark.ml.feature import QuantileDiscretizer
​
    discretizer = QuantileDiscretizer(
        numBuckets=input["num_buckets"], inputCol=input["col"], outputCol="_"
    ).fit(data)
​
    return discretizer.getSplits()

pyspark==2.4.2
boto3==1.9.78
msgpack==0.6.1
numpy>=1.13.3,<2
requirements-parser==0.2.0
packaging==19.0.0
pillow==6.1.0
regex==2017.4.5
requests==2.21.0