Failed to train : : 'NoneType' object is not iterable

com.dataiku.dip.io.SocketBlockLinkKernelException: Failed to train : <class 'TypeError'> : 'NoneType' object is not iterable
    at com.dataiku.dip.io.SocketBlockLinkInteraction.throwExceptionFromPython(SocketBlockLinkInteraction.java:302)
    at com.dataiku.dip.io.SocketBlockLinkInteraction$AsyncResult.checkException(SocketBlockLinkInteraction.java:215)
    at com.dataiku.dip.io.SocketBlockLinkInteraction$AsyncResult.get(SocketBlockLinkInteraction.java:190)
    at com.dataiku.dip.io.SingleCommandKernelLink$1.call(SingleCommandKernelLink.java:211)
    at com.dataiku.dip.analysis.ml.prediction.PredictionTrainAdditionalThread.process(PredictionTrainAdditionalThread.java:76)
    at com.dataiku.dip.analysis.ml.shared.PRNSTrainThread.run(PRNSTrainThread.java:170)

FIT/PROCESS WITH Step:FeatureSelectionStep
/opt/dataiku-dss-12.1.3/python39.packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [0 0] are constant.
  warnings.warn("Features %s are constant." % constant_features_idx, UserWarning)
/opt/dataiku-dss-12.1.3/python39.packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in true_divide
  f = msb / msw
/opt/dataiku-dss-12.1.3/python39.packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [0] are constant.
  warnings.warn("Features %s are constant." % constant_features_idx, UserWarning)
[2023-10-20 17:05:30,565] [125197/MainThread] [INFO] [dataiku.doctor.utils.listener] END -  Preprocessing train set
[2023/10/20-17:05:30.566] [MRT-16096] [INFO] [dku.block.link.interaction]  - Check result for nullity exceptionIfNull=true result=null
Traceback (most recent call last):
  File "/opt/dataiku-dss-12.1.3/python/dataiku/doctor/server.py", line 45, in serve
    ret = api_command(arg)
  File "/opt/dataiku-dss-12.1.3/python/dataiku/doctor/dkuapi.py", line 45, in aux
    return api(**kwargs)
  File "/opt/dataiku-dss-12.1.3/python/dataiku/doctor/commands.py", line 343, in train_prediction_models_nosave
    transformed_train = pipeline.fit_and_process(train_df)
  File "/opt/dataiku-dss-12.1.3/python/dataiku/doctor/preprocessing/dataframe_preprocessing.py", line 2537, in fit_and_process
    new_mf = step.fit_and_process(input_df, cur_mf, result, self.generated_features_mapping)
  File "/opt/dataiku-dss-12.1.3/python/dataiku/doctor/prediction/feature_selection.py", line 66, in fit_and_process
    self.selection = get_feature_selector(
  File "/opt/dataiku-dss-12.1.3/python/dataiku/doctor/prediction/feature_selection.py", line 171, in fit
    names = self.get_pruned_names(mf, target)
  File "/opt/dataiku-dss-12.1.3/python/dataiku/doctor/prediction/feature_selection.py", line 205, in get_pruned_names