class_docstr: A Pytorch DataSet class to process a multimodal pd.DataFrame. It first uses a preprocessor to
  produce model-agnostic features. Then, each processor prepares customized data for one modality
  per model. For code simplicity, here we treat ground-truth label as one modality. This class is
  independent of specific data modalities and models.
docstr: Assume that all modalities have the same sample number.
  
  Returns
  -------
  Sample number in this dataset.
code: def __len__(self):
      """
      Assume that all modalities have the same sample number.
  
      Returns
      -------
      Sample number in this dataset.
      """
      return self.lengths[0]
  
file_path: autogluon/multimodal/src/autogluon/multimodal/data/dataset.py
imports: import logging
  from typing import Dict, List, Optional, Union
  import pandas
  import torch
  from ..constants import AUTOMM, GET_ITEM_ERROR_RETRY
  from .preprocess_dataframe import MultiModalFeaturePreprocessor
  from .utils import apply_data_processor, apply_df_preprocessor, get_per_sample_features
label_desc: Use image node_id 2 for calling the BaseDataset obj's underlying member method code with example usage: obj.__len__() and returns: self