tabular/src/autogluon/tabular/models/abstract/abstract_model.py
def _preprocess(self, X: pd.DataFrame, **kwargs):
        """
        Data transformation logic should be added here.
        In bagged ensembles, preprocessing code that lives in `_preprocess` will be executed on each child model once per inference call.
        If preprocessing code could produce different output depending on the child model that processes the input data, then it must live here.
        When in doubt, put preprocessing code here instead of in `_preprocess_nonadaptive`.
        """
        return X

        Data transformation logic should be added here.
        In bagged ensembles, preprocessing code that lives in `_preprocess` will be executed on each child model once per inference call.
        If preprocessing code could produce different output depending on the child model that processes the input data, then it must live here.
        When in doubt, put preprocessing code here instead of in `_preprocess_nonadaptive`.
        
Data transformation logic should be added here.
In bagged ensembles, preprocessing code that lives in `_preprocess` will be executed on each child model once per inference call.
If preprocessing code could produce different output depending on the child model that processes the input data, then it must live here.
When in doubt, put preprocessing code here instead of in `_preprocess_nonadaptive`.
Data transformation logic should be added here . In bagged ensembles preprocessing code that lives in ` _preprocess ` will be executed on each child model once per inference call . If preprocessing code could produce different output depending on the child model that processes the input data then it must live here . When in doubt put preprocessing code here instead of in ` _preprocess_nonadaptive ` .
def _preprocess(self, X: pd.DataFrame, **kwargs):
        return X
def _preprocess ( self , X : pd . DataFrame , ** kwargs ) : return X
Data transformation logic should be added here.
Data transformation logic should be added here .
"""
        Data transformation logic should be added here.
        In bagged ensembles, preprocessing code that lives in `_preprocess` will be executed on each child model once per inference call.
        If preprocessing code could produce different output depending on the child model that processes the input data, then it must live here.
        When in doubt, put preprocessing code here instead of in `_preprocess_nonadaptive`.
        """
self
X
pd.DataFrame