code: def generate_output_images(
      image_sizes: Iterable[Tuple[int, int]], num_samples=10
  ):
      num_classes = 5
      task = Task(TaskTypesEnum.classification)
      data_type = DataTypesEnum.image
      target = np.random.randint(0, num_classes, (num_samples, 1))
  
      outputs = []
      for img_shape in image_sizes:
          idx = np.arange(0, num_samples)
          features = np.random.random_sample((num_samples, *img_shape))
          predict = 0.9 * features
          output_data = OutputData(
              idx=idx,
              features=features,
              predict=predict,
              target=target,
              task=task,
              data_type=data_type,
          )
          outputs.append(output_data)
      return outputs
  
file_path: FEDOT/test/unit/data/test_data_merge_image.py
imports: import pytest
  from typing import Tuple, Iterable
  import numpy
  from fedot.core.data.data import OutputData
  from fedot.core.data.merge.data_merger import DataMerger
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.data.array_utilities import atleast_4d
label_desc: Use image node_id 1 for calling a global function with example usage: generate_output_images(image_sizes, num_samples) and returns: outputs