HMA sampling fails when child table is given a distribution other than beta

[amontanez24]

Environment Details

Please indicate the following details about the environment in which you found the bug:

• SDV version: 1.17.2
• Python version: Any
• Operating System: Any

Error Description

If you use a distribution other than beta on a child column when using HMASynthesizer, it fails with the following warning:

Preprocess Tables: 100%|██████████| 2/2 [00:00<00:00,  3.55it/s]

Learning relationships:
(1/1) Tables 'hotels' and 'guests' ('hotel_id'): 100%|██████████| 10/10 [00:06<00:00,  1.60it/s]

Modeling Tables: 100%|██████████| 2/2 [00:01<00:00,  1.17it/s]
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-0fddabb97028> in <cell line: 16>()
     14 # )
     15 synthesizer.fit(data)
---> 16 synthetic_data = synthesizer.sample()
     17 
     18 synthesizer.get_learned_distributions('hotels')

11 frames
/usr/local/lib/python3.10/dist-packages/scipy/stats/_distn_infrastructure.py in ppf(self, q, *args, **kwds)
   2233 
   2234         """
-> 2235         args, loc, scale = self._parse_args(*args, **kwds)
   2236         q, loc, scale = map(asarray, (q, loc, scale))
   2237         args = tuple(map(asarray, args))

TypeError: _parse_args() got an unexpected keyword argument 'a'

It seems that somewhere we hardcoded a check for beta parameters.

Steps to reproduce

from sdv.datasets.demo import download_demo
from sdv.multi_table import HMASynthesizer

data, metadata = download_demo('multi_table', 'fake_hotels')

synthesizer = HMASynthesizer(metadata)
guests_distributions = {'room_type': 'norm', 'amenities_fee': 'norm'}
synthesizer.set_table_parameters(
    'guests', {'numerical_distributions': guests_distributions}
)
# synthesizer.set_table_parameters(
#     'hotels',
#     {'numerical_distributions': {'rating': 'norm'}},
# )
synthesizer.fit(data)
synthetic_data = synthesizer.sample()

synthesizer.get_learned_distributions('hotels')