class_docstr: Preprocess one multimodal pd.DataFrame including image paths, image bytearrays, texts, numerical features,
  and categorical features. Each modality may have multiple columns.
  The preprocessor is designed to output model-agnostic features.
class_docstr_tok: ["Preprocess","one","multimodal","pd.DataFrame","including","image","paths",",","image","bytearrays",",","texts",",","numerical","features",",","and","categorical","features",".","Each","modality","may","have","multiple","columns",".","The","preprocessor","is","designed","to","output","model-agnostic","features","."]
code: def label_type(self):
      return self._column_types[self._label_column]
  
imports: import logging
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from nptyping import NDArray
  from omegaconf import DictConfig, OmegaConf
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import MinMaxScaler, StandardScaler
  from autogluon.features import CategoryFeatureGenerator
  from ..constants import AUTOMM, CATEGORICAL, DOCUMENT, DOCUMENT_IMAGE, IDENTIFIER, IMAGE, IMAGE_BYTEARRAY, IMAGE_PATH, LABEL, NER, NER_ANNOTATION, NULL, NUMERICAL, OVD, ROIS, SEMANTIC_SEGMENTATION, SEMANTIC_SEGMENTATION_GT, SEMANTIC_SEGMENTATION_IMG, TEXT, TEXT_NER
  from .label_encoder import CustomLabelEncoder
label_desc: Use image node_id 18 for calling the MultiModalFeaturePreprocessor obj's underlying member method code with example usage: obj.label_type() and returns: self