class_docstr: Dictionary with InputData as values and primary node names as keys 
class_docstr_tok: ["Dictionary","with","InputData","as","values","and","primary","node","names","as","keys"]
docstr: Function for extraction data_source from MultiModalData
  :param source_name: string with user-specified name of source
  :return target_data: selected source InputData
docstr_tok: ["Function","for","extraction","data_source","from","MultiModalData",":","param","source_name",":","string","with","user-specified","name","of","source",":","return","target_data",":","selected","source","InputData"]
code: def extract_data_source(self, source_name):
      """
      Function for extraction data_source from MultiModalData
      :param source_name: string with user-specified name of source
      :return target_data: selected source InputData
      """
      full_target_name = [
          key
          for key, _ in self.items()
          if source_name == key.split("/")[-1]
      ][0]
      source_data = self[full_target_name]
      return source_data
  
imports: from __future__ import annotations
  from functools import partial
  from typing import List, Optional, Union, Dict
  import numpy
  from fedot.core.data.data import InputData
  from fedot.core.data.data import process_target_and_features, array_to_input_data, get_df_from_csv, PathType, POSSIBLE_TABULAR_IDX_KEYWORDS, POSSIBLE_TS_IDX_KEYWORDS
  from fedot.core.data.data_detection import TextDataDetector, TimeSeriesDataDetector
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
label_desc: Use image node_id 11 for calling the MultiModalData obj's underlying member method code with example usage: obj.extract_data_source(source_name) and returns: source_data