Error when running catboost on valid training and target sets version 1.1.1

def train_model_pandas(self, train_df: pd.DataFrame, valid_df: pd.DataFrame = None):

labels = some function call that extracts labels successfully
# this part works
training = train_df.merge(labels, on="common key")

y = training[['target variable']]
Y = pd.get_dummies(y)
X = np.vstack(training['feature_scaled'].fillna(0))
# I checked if any of these were null, they weren't and are of the same number of rows (422,8): target shape, (422,23) training shape. Target is pandas dataframe and X is numpy

#now call catboost here
#model training fails

self.__model = CatBoostClassifier(
iterations = 10000,
learning_rate=0.01,
loss_function='MultiCrossEntropy'
).fit(X,Y)

Problem:

I get the following error:

94, in train_model_pandas
).fit(X,Y)
File "/usr/local/lib/python3.10/site-packages/catboost/core.py", line 5128, in fit
self._fit(X, y, cat_features, text_features, embedding_features, None, sample_weight, None, None, None, None, baseline, use_best_model,
File "/usr/local/lib/python3.10/site-packages/catboost/core.py", line 2355, in _fit
self._train(
File "/usr/local/lib/python3.10/site-packages/catboost/core.py", line 1759, in _train
self._object._train(train_pool, test_pool, params, allow_clear_pool, init_model._object if init_model else None)
SystemError: returned a result with an exception set

catboost version: 1.1.1
Operating System: windows 10, pyspark kernel but I am using pandas for training due to the nature of predictions.
CPU: NA
GPU: NA