content: import torch
  import InternVideo
  
  text_cand = ["an airplane is taking off", "an airplane is flying", "a dog is chasing a ball"]
  video = InternVideo.load_video("./data/demo.mp4").cuda()
  
  model = InternVideo.load_model("./models/InternVideo-MM-L-14.ckpt").cuda()
  text = InternVideo.tokenize(
      text_cand
  ).cuda()
  
  with torch.no_grad():
      text_features = model.encode_text(text)
      video_features = model.encode_video(video.unsqueeze(0))
  
      video_features = torch.nn.functional.normalize(video_features, dim=1)
      text_features = torch.nn.functional.normalize(text_features, dim=1)
      t = model.logit_scale.exp()
      probs = (video_features @ text_features.T * t).softmax(dim=-1).cpu().numpy()
  
  print("Label probs: ")  # [[9.5619422e-01 4.3805469e-02 2.0393253e-07]]
  for t, p in zip(text_cand, probs[0]):
      print("{:30s}: {:.4f}".format(t, p))
  