def predict_html(image_tensor: Tensor) -> List[str]:
    """
    Predict HTML structure from the input image

    Note uses the global structure_model and structure_vocab
    """
    pred_tensor = _autoregressive_decode(
        model=structure_model,
        image=image_tensor,
        prefix=[structure_vocab.token_to_id("[html]")],
        max_decode_len=512,
        eos_id=structure_vocab.token_to_id("<eos>"),
        token_whitelist=[structure_vocab.token_to_id(i) for i in VALID_HTML_TOKEN],
        token_blacklist=None,
    )

    # Convert token id to token text
    pred_tensor = pred_tensor.detach().cpu().numpy()[0]
    token_str = structure_vocab.decode(pred_tensor, skip_special_tokens=False)
    token_list = html_str_to_token_list(token_str)
    return token_list

    Predict HTML structure from the input image

    Note uses the global structure_model and structure_vocab
    
src/openparse/tables/unitable/core.py