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","."]
docstr: Preprocess document data by collecting their paths together. The current version does not
  support cases where one document column has multiple documents.
  This function needs to be called preceding the document processor in "process_document.py".
  
  Parameters
  ----------
  df
      The multimodal pd.DataFrame.
  
  Returns
  -------
  document_features
      All the image data stored in a dictionary.
  document_types
      The column types of these document data.
code: def transform_document(
      self,
      df: pd.DataFrame,
  ) -> Tuple[Dict[str, List[List[str]]], Dict[str, str]]:
      """
      Preprocess document data by collecting their paths together. The current version does not
      support cases where one document column has multiple documents.
      This function needs to be called preceding the document processor in "process_document.py".
  
      Parameters
      ----------
      df
          The multimodal pd.DataFrame.
  
      Returns
      -------
      document_features
          All the image data stored in a dictionary.
      document_types
          The column types of these document data.
      """
      assert (
          self._fit_called or self._fit_x_called
      ), "You will need to first call preprocessor.fit_x() before calling preprocessor.transform_document."
      document_features = {}
      document_types = {}
      for col_name in self._document_feature_names:
          col_value = df[col_name]
          col_type = self._column_types[col_name]
  
          processed_data = col_value.apply(
              lambda ele: str(ele).split(";")
          ).tolist()
  
          document_features[col_name] = processed_data
          document_types[col_name] = self._column_types[col_name]
  
      return document_features, document_types
  
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 33 for calling the MultiModalFeaturePreprocessor obj's underlying member method code with example usage: obj.transform_document(df) and returns: document_features,  document_types