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(
              {"data": kitten_image_bytes}
              if l == "tiger_cat"
              else {"data": dog_image_bytes}
          )
      return test_data, labels
  
imports: import json
  import math
  import random
  import sys
  from pathlib import Path
  import pytest
  from torchvision.models.resnet import ResNet18_Weights
  from ts.handler_utils.micro_batching import MicroBatching
  from ts.torch_handler.image_classifier import ImageClassifier
  from ts.torch_handler.unit_tests.test_utils.mock_context import MockContext
  from ts.torch_handler.unit_tests.test_utils.model_dir import copy_files, download_model
label_desc: Use image node_id 8 for calling a global function with example usage: mixed_batch(kitten_image_bytes, dog_image_bytes, request) and returns: test_data,  labels