code: def test_pretrained(self):
      for idx in torch.topk(self.outputs, k=1).indices.tolist():
          prob = torch.softmax(self.outputs, dim=0)[idx].item()
          label = self.labels_map[idx]
          p = prob * 100
          print(f"[{idx}] {label:<75} ({p:.2f}%)")
          self.assertEqual(
              "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca",
              self.labels_map[idx],
          )
  
file_path: towhee/tests/unittests/models/vit/test_vit.py
imports: import unittest
  import os
  import json
  import torch
  from tests.unittests import VIT_DIR
  from torchvision import transforms
  from towhee.utils.pil_utils import PILImage
  from towhee.models import vit
label_desc: Use image node_id 1 for calling the VisionTransformerTest obj's underlying member method code with example usage: obj.test_pretrained() without return types