Issue with the model.recommend() from the 'implicit' python library while I was using it to create an ALS based recommendation system !!!

[anirudhasahu92]

# Input
user_idx = 75
model.recommend(user_idx, sparse_item_user, N=10)

# output
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[164], line 2
      1 user_idx = 75
----> 2 model.recommend(user_idx, sparse_item_user, N=10)

File C:\anaconda3\Lib\site-packages\implicit\cpu\matrix_factorization_base.py:49, in MatrixFactorizationBase.recommend(self, userid, user_items, N, filter_already_liked_items, filter_items, recalculate_user, items)
     47     user_count = 1 if np.isscalar(userid) else len(userid)
     48     if user_items.shape[0] != user_count:
---> 49         raise ValueError("user_items must contain 1 row for every user in userids")
     51 user = self._user_factor(userid, user_items, recalculate_user)
     53 item_factors = self.item_factors

ValueError: user_items must contain 1 row for every user in userids

I am getting this error. Raising the issue. What is the fix?