Rolling Average with variable min_periods from another column

I have a dataframe with multiple accounts across the last few years and am trying to get the rolling average of a column, per account, which is easy enough. However I also need to have the min_periods be variable. I have a column for the min_periods in the dataframe (minuploads) and the window is always 10. I just cannot find how to go about doing this. End of the day, I am trying to take a rolling average of the last 10 values, and each account falls into a different sort of category which requires a different minimum number of previous values which I have set as minuploads. If there are not enough previous values, I dont want the rolling average to calculate and will use a modeled value for those rows.

Here is a sample of the dataframe

Initially I didnt use a variable min_periods and used this code for the rolling average:

df['ma'] = df.groupby('accountID')['diffs'].transform(lambda x: x.rolling(10,5).mean())

Which worked great, but then decided I need to do a variable min_period. I tried this but I get a key error

df['ma'] = df.groupby('accountID')['diffs'].transform(lambda x: x.rolling(10,x['minuploads']).mean())

Also tried this to make sure the groupby had both columns, but still get a KeyError: 'minuploads'

df['ma'] = df.groupby('accountID')[['diffs','minuploads']].transform(lambda x: x.rolling(10,x['minuploads']).mean())

minuploads shouldn't be a column because it's not per-row, it's per-account. You can just use a dict, or a Series if you prefer.

I've had to change the numbers to work with this example data. (8 and 10 to 2 and 3)

minuploads = {
    1001: 2,
    1002: 3,
}
df['ma'] = df.groupby('accountID')['diffs'].transform(
    lambda x: x.rolling(10, minuploads[x.name]).mean())

Result:

   accountID        date  diffs        ma
0       1001  2020-01-12      7       NaN
1       1001  2020-01-19      7  7.000000
2       1001  2020-01-20      1  5.000000
3       1001  2020-01-26      6  5.250000
4       1002  2020-01-10      2       NaN
5       1002  2020-01-13      3       NaN
6       1002  2020-01-16      3  2.666667
7       1002  2020-01-18      2  2.500000