ludwig/data/preprocessing.py [115:145]:
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    for feature in features:
        if 'preprocessing' in feature:
            preprocessing_parameters = merge_dict(
                global_preprocessing_parameters[feature[TYPE]],
                feature['preprocessing']
            )
        else:
            preprocessing_parameters = global_preprocessing_parameters[
                feature[TYPE]
            ]

        # deal with encoders that have fixed preprocessing
        if 'encoder' in feature:
            encoders_registry = get_from_registry(
                feature[TYPE],
                input_type_registry
            ).encoder_registry
            encoder_class = encoders_registry[feature['encoder']]
            if hasattr(encoder_class, 'fixed_preprocessing_parameters'):
                encoder_fpp = encoder_class.fixed_preprocessing_parameters

                preprocessing_parameters = merge_dict(
                    preprocessing_parameters,
                    resolve_pointers(encoder_fpp, feature, 'feature.')
                )

        handle_missing_values(
            dataset_df,
            feature,
            preprocessing_parameters
        )
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ludwig/data/preprocessing.py [166:197]:
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    for feature in features:
        if 'preprocessing' in feature:
            preprocessing_parameters = merge_dict(
                global_preprocessing_parameters[feature[TYPE]],
                feature['preprocessing']
            )
        else:
            preprocessing_parameters = global_preprocessing_parameters[
                feature[TYPE]
            ]

        # deal with encoders that have fixed preprocessing
        if 'encoder' in feature:
            encoders_registry = get_from_registry(
                feature[TYPE],
                input_type_registry
            ).encoder_registry

            encoder_class = encoders_registry[feature['encoder']]
            if hasattr(encoder_class, 'fixed_preprocessing_parameters'):
                encoder_fpp = encoder_class.fixed_preprocessing_parameters

                preprocessing_parameters = merge_dict(
                    preprocessing_parameters,
                    resolve_pointers(encoder_fpp, feature, 'feature.')
                )
                
        handle_missing_values(
            dataset_df,
            feature,
            preprocessing_parameters
        )
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