text: from torchvision import transforms
  from PyTorch_Pretrained_ViT.pytorch_pretrained_vit.model import ViT
  import json
  import torch
  import datetime
  from PIL import Image
  
  model_name = 'B_32_imagenet1k'
  model = ViT(model_name, pretrained=True)
  
  img = Image.open("PyTorch_Pretrained_ViT/examples/simple/img.jpg")
  
  tfms = transforms.Compose([transforms.Resize(model.image_size), transforms.ToTensor(),
                             transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),])
  img = tfms(img).unsqueeze(0)
  
  
  # Load class names
  labels_map = json.load(open('PyTorch_Pretrained_ViT/examples/simple/labels_map.txt'))
  labels_map = [labels_map[str(i)] for i in range(1000)]
  
  model.eval()
  with torch.no_grad():
      tik = datetime.datetime.now()
      outputs, feature_maps = model(img)
      output = outputs[0].squeeze(0)
      print(datetime.datetime.now() - tik)
  print('-----')
  for idx in torch.topk(output, k=3).indices.tolist():
      prob = torch.softmax(output, -1)[idx].item()
      print('[{idx}] {label:<75} ({p:.2f}%)'.format(idx=idx, label=labels_map[idx], p=prob*100))
  
  