I have a data frame with multi-index columns. 

From this data frame I need to remove the rows with NaN values in a subset of columns. 

I am trying to use the `subset` option of `pd.dropna` but I do not manage to find the way to specify the subset of columns. I have tried using `pd.IndexSlice` but this does not work.

In the example below I need to get ride of the last row.

```
import pandas as pd

# ---
a = [1, 1, 2, 2, 3, 3]
b = ["a", "b", "a", "b", "a", "b"]
col = pd.MultiIndex.from_arrays([a[:], b[:]])
val = [
    [1, 2, 3, 4, 5, 6],
    [None, None, 1, 2, 3, 4],
    [None, 1, 2, 3, 4, 5],
    [None, None, 5, 3, 3, 2],
    [None, None, None, None, 5, 7],
]
# ---
df = pd.DataFrame(val, columns=col)
# ---
print(df)
# ---
idx = pd.IndexSlice
df.dropna(axis=0, how="all", subset=idx[1:2, :])
# ---
print(df)

```

Using the `thresh` option is an alternative but if possible I would like to use `subset` and `how='all'`