code: def mixed_batch(kitten_image_bytes, dog_image_bytes, request):
      batch_size = request.param
      labels = [
          "tiger_cat" if random.random() > 0.5 else "golden_retriever"
          for _ in range(batch_size)
      ]
      test_data = []
      for l in labels:
          test_data.append(
              kitten_image_bytes
              if l == "tiger_cat"
              else dog_image_bytes
          )
      return test_data, labels
  
imports: import asyncio
  import json
  import random
  import shutil
  from pathlib import Path
  from unittest.mock import patch
  from zipfile import ZIP_STORED, ZipFile
  import pytest
  import requests
  import test_utils
  import yaml
  from model_archiver import ModelArchiverConfig
  from torchvision.models.resnet import ResNet18_Weights
  from ts.torch_handler.unit_tests.test_utils.model_dir import download_model
label_desc: Use image node_id 4 for calling a global function with example usage: mixed_batch(kitten_image_bytes, dog_image_bytes, request) and returns: test_data,  labels