code: def create_fake_yolo_labels(
      self, no_classes: int = 10, objects_per_image: int = 13
  ):
      random.seed(42)
  
      n_data = len(self.api_workflow_client.get_filenames())
  
      self.folder_path_labels = tempfile.mkdtemp()
      label_names = [f"img_{i}.txt" for i in range(n_data)]
      self.label_names = label_names
      for filename_label in label_names:
          path = os.path.join(self.folder_path_labels, filename_label)
          with open(path, "a") as the_file:
              for i in range(objects_per_image):
                  class_id = random.randint(0, no_classes - 1)
                  x = random.uniform(0.1, 0.9)
                  y = random.uniform(0.1, 0.9)
                  w = random.uniform(0.1, 1.0)
                  h = random.uniform(0.1, 1.0)
                  line = f"{class_id} {x} {y} {w} {h}\n"
                  the_file.write(line)
      yaml_dict = {"names": [f"class{i}" for i in range(no_classes)]}
      self.label_names_file = tempfile.mktemp(
          ".yaml", "data", dir=self.folder_path_labels
      )
      with open(self.label_names_file, "w") as file:
          yaml.dump(yaml_dict, file)
  
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 3 for calling the TestCLICrop obj's underlying member method code with example usage: obj.create_fake_yolo_labels(no_classes, objects_per_image) without return types