code: def run_multi_modal_pipeline(
      files_path: str, visualization=False
  ) -> float:
      task = Task(TaskTypesEnum.classification)
      images_size = (224, 224)
  
      data = prepare_multi_modal_data(files_path, task, images_size)
  
      fit_data, predict_data = train_test_data_setup(
          data, shuffle=True, split_ratio=0.6
      )
  
      automl_model = Fedot(problem="classification", timeout=15)
      pipeline = automl_model.fit(
          features=fit_data, target=fit_data.target
      )
  
      if visualization:
          pipeline.show()
  
      prediction = pipeline.predict(predict_data, output_mode="labels")
  
      err = calculate_validation_metric(predict_data, prediction)
  
      print(f"F1 micro for validation sample is {err}")
  
      return err
  
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 3 for calling a global function with example usage: run_multi_modal_pipeline(files_path, visualization) and returns: err