code: def prepare_multi_modal_data(
      files_path: str, task: Task, images_size: tuple = (128, 128)
  ) -> MultiModalData:
      """
      Imports data from 3 different sources (table, images and text)
  
      :param files_path: path to data
      :param task: task to solve
      :param images_size: the requested size in pixels, as a 2-tuple of (width, height)
      :return: MultiModalData object which contains table, text and image data
      """
  
      path = os.path.join(str(fedot_project_root()), files_path)
      # unpacking of data archive
      unpack_archived_data(path)
      # import of table data
      data_num = InputData.from_json_files(
          path,
          fields_to_use=["votes", "rating"],
          label="genres",
          task=task,
          is_multilabel=True,
          shuffle=False,
      )
  
      class_labels = data_num.target
  
      img_files_path = f"{files_path}/*.jpeg"
      img_path = os.path.join(str(fedot_project_root()), img_files_path)
  
      # import of image data
      data_img = InputData.from_image(
          images=img_path,
          labels=class_labels,
          task=task,
          target_size=images_size,
      )
      # import of text data
      data_text = InputData.from_json_files(
          path,
          fields_to_use=["plot"],
          label="genres",
          task=task,
          data_type=DataTypesEnum.text,
          is_multilabel=True,
          shuffle=False,
      )
  
      data = MultiModalData(
          {
              "data_source_img": data_img,
              "data_source_table": data_num,
              "data_source_text": data_text,
          }
      )
  
      return data
  
file_path: FEDOT/examples/advanced/multi_modal_pipeline.py
imports: import os
  from typing import Union
  from sklearn.metrics import f1_score
  from cases.dataset_preparation import unpack_archived_data
  from fedot import Fedot
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 2 for calling a global function with example usage: prepare_multi_modal_data(files_path, task, images_size) and returns: data