code: def get_image_classification_data():
      task = Task(TaskTypesEnum.classification)
      # Imitate small pictures (5 x 5) with this array
      features = np.random.random((10, 5, 5, 2))
      target = np.full((10, 1), 1, dtype=float)
      input_data = InputData(
          idx=np.arange(0, len(features)),
          features=features,
          target=target,
          task=task,
          data_type=DataTypesEnum.image,
      )
      return input_data
  
imports: from functools import partial
  from typing import Callable
  import numpy
  import pandas
  import pytest
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.optimisers.objective.data_source_splitter import DataSourceSplitter
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import split_data
  from fedot.core.data.cv_folds import cv_generator
  from test.unit.pipelines.test_decompose_pipelines import get_classification_data
  from test.unit.tasks.test_forecasting import get_ts_data
label_desc: Use image node_id 5 for calling a global function with example usage: get_image_classification_data() and returns: input_data