device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     inputs = {k: v.to(device) for k, v in inputs.items()}
     model = model.to(device)
     # forward pass
     with torch.no_grad():
         outputs = model(**inputs)
         logits = outputs.logits
     return logits

Now, pass your input to the model and return the logits:

logits = run_inference(trained_model, sample_test_video["video"])

Decoding the logits, we get: