code: def setUpClass(cls):
      trunk = torchvision.models.resnet18(pretrained=True)
      trunk.fc = torch.nn.Identity()
      trunk = trunk.to(TEST_DEVICE)
  
      cls.model = trunk
  
      transform = transforms.Compose(
          [
              transforms.Resize(64),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
              ),
          ]
      )
  
      cls.dataset = datasets.FakeData(
          size=200, image_size=(3, 64, 64), transform=transform
      )
  
      cls.train_vectors = [
          cls.dataset[i][0] for i in range(len(cls.dataset))
      ]
  
imports: import os
  import unittest
  import uuid
  import faiss
  import torch
  import torchvision
  from torchvision import datasets, transforms
  from pytorch_metric_learning.utils.inference import InferenceModel
  from ..None import TEST_DEVICE
label_desc: Use image node_id 1 for calling the TestInference obj's underlying member method code with example usage: obj.setUpClass(cls) without return types