code: def create_fake_dataset(self):
      n_data = len(self.api_workflow_client.get_filenames())
      self.dataset = torchvision.datasets.FakeData(
          size=n_data, image_size=(3, 32, 32)
      )
  
      self.folder_path = tempfile.mkdtemp()
      sample_names = [f"img_{i}.jpg" for i in range(n_data)]
      self.sample_names = sample_names
      for sample_idx in range(n_data):
          data = self.dataset[sample_idx]
          path = os.path.join(
              self.folder_path, sample_names[sample_idx]
          )
          data[0].save(path)
  
imports: import os
  import random
  import re
  import sys
  import tempfile
  import torchvision
  import yaml
  from hydra.experimental import compose, initialize
  import lightly
  from lightly.data import LightlyDataset
  from lightly.utils.bounding_box import BoundingBox
  from lightly.utils.cropping.crop_image_by_bounding_boxes import crop_dataset_by_bounding_boxes_and_save
  from lightly.utils.cropping.read_yolo_label_file import read_yolo_label_file
  from tests.api_workflow.mocked_api_workflow_client import MockedApiWorkflowClient, MockedApiWorkflowSetup
label_desc: Use image node_id 2 for calling the TestCLICrop obj's underlying member method code with example usage: obj.create_fake_dataset() without return types