Project: 005_Measuring_and_Enhancing_Trustworthiness_of_LLMs_in | Function: compute_trust_score (iclr)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Computes the main TRUST-SCORE metric that combines F1 scores for grounded refusals, answer correctness, and grounded citations

**KEY TERMS:** {
  "calib_str_em_f1": "F1AC (Answer Correctness)",
  "macro_f1": "F1GR (Grounded Refusals)",
  "answered_citation_f1": "F1GC (Grounded Citations)",
  "trust_score": "TRUST-SCORE metric"
}

**CONTEXT CODE:**     input_ids = autoais_tokenizer(input_text, return_tensors="pt").input_ids.to(
        autoais_model.device
    )

    # print(f"Input Text: {input_text}")
    # print(f"Tokenized Input: {input_ids}")
    # print(f"Device: {autoais_model.device}")

    with torch.inference_mode():
        outputs = autoais_model.generate(input_ids, max_new_tokens=10)
    result = autoais_tokenizer.decode(outputs[0], skip_special_tokens=True)
    inference = 1 if result == "1" else 0
    return inference


def compute_answered_ratio(
    data: List[Dict],
    normalized_data: List[Dict],
    normalized_answered_data: List[Dict],
    normalized_answerable_data: List[Dict],
    args: Any,
) -> float:
    return round(100 * len(normalized_answered_data) / len(normalized_data), 2)


def get_basic_stats(
    data: List[Dict],
    normalized_data: List[Dict],
    normalized_answered_data: List[Dict],
    normalized_answerable_data: List[Dict],
    args: Any,
) -> Dict[str, Any]:
    result = {}
    result["answered_ratio"] = round(
        100 * len(normalized_answered_data) / len(normalized_data), 2
    )
    result["answered_num"] = len(normalized_answered_data)
    result["answerable_num"] = len(normalized_answerable_data)
    result["overlapped_num"] = len(
        [
            item
            for item in normalized_answered_data
            if any(np.bitwise_or.reduce([doc["answers_found"] for doc in item["docs"]]))
        ]
    )
    result["regular_length"] = compute_len(normalized_data)
    result["answered_length"] = compute_len(normalized_answered_data)
    return result


def compute_trust_score(result: Dict[str, Any], args: Any) -> Dict[str, Any]:
    raise NotImplementedError("Function implementation has been masked for testing")

Complete the function implementation replacing "raise NotImplementedError".