Using polars .rolling and .agg, how do I get the original column back, without having to join back with the original column, or without having to use .over ?

Example:

import polars as pl

dates = [
    "2020-01-01 13:45:48",
    "2020-01-01 16:42:13",
    "2020-01-01 16:45:09",
    "2020-01-02 18:12:48",
    "2020-01-03 19:45:32",
    "2020-01-08 23:16:43",
]
df = pl.DataFrame({"dt": dates, "a": [3, 7, 5, 9, 2, 1]}).with_columns(
    pl.col("dt").str.strptime(pl.Datetime).set_sorted()
)

Provides me with a small polars dataframe:

When I apply a rolling aggregations, I get the new columns back, but not the original columns:

out = df.rolling(index_column="dt", period="2d").agg(
    [
        pl.sum("a").alias("sum_a"),
        pl.min("a").alias("min_a"),
        pl.max("a").alias("max_a"),
        pl.col('a')
    ]
)

which gives:

How can I get the original a column. I don’t want to join and I don’t want to use .over as I need the group_by of the rolling later on and .over does not work with .rolling