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 image data by collecting their paths together. If one sample has multiple images
  in an image column, assume that their image paths are separated by ";".
  This function needs to be called preceding the image processor in "process_image.py".
  
  Parameters
  ----------
  df
      The multimodal pd.DataFrame.
  
  Returns
  -------
  image_features
      All the image data stored in a dictionary.
  image_types
      The column types of these image data, e.g., image_path, image_bytearray or image_identifier.
code: def transform_image(
      self,
      df: pd.DataFrame,
  ) -> Tuple[Dict[str, List[List[str]]], Dict[str, str]]:
      """
      Preprocess image data by collecting their paths together. If one sample has multiple images
      in an image column, assume that their image paths are separated by ";".
      This function needs to be called preceding the image processor in "process_image.py".
  
      Parameters
      ----------
      df
          The multimodal pd.DataFrame.
  
      Returns
      -------
      image_features
          All the image data stored in a dictionary.
      image_types
          The column types of these image data, e.g., image_path, image_bytearray or image_identifier.
      """
      assert (
          self._fit_called or self._fit_x_called
      ), "You will need to first call preprocessor.fit_x() before calling preprocessor.transform_image."
  
      image_features = {}
      image_types = {}
      for col_name in self._image_feature_names:
          col_value = df[col_name]
          col_type = self._column_types[col_name]
  
          if col_type == ROIS:
              processed_data = df[col_name].tolist()
          elif col_type in [IMAGE_PATH, IMAGE]:
              processed_data = col_value.apply(
                  lambda ele: str(ele).split(";")
              ).tolist()
          elif col_type == IMAGE_BYTEARRAY:
              processed_data = col_value.apply(
                  lambda ele: ele if isinstance(ele, list) else [ele]
              ).tolist()
          elif col_type == f"{IMAGE}_{IDENTIFIER}":
              processed_data = col_value
          else:
              raise ValueError(
                  f"Unknown image type {col_type} for column {col_name}"
              )
  
          image_features[col_name] = processed_data
          image_types[col_name] = self._column_types[col_name]
  
      return image_features, image_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 32 for calling the MultiModalFeaturePreprocessor obj's underlying member method code with example usage: obj.transform_image(df) and returns: image_features,  image_types