def parse_top_logprobs(top_logprobs: dict[str, float]) -> OrderedDict[int, float]:
    """
    Given a map from tokens to logprobs, return a map from distribution values (integers on the
    range [0, 10]) to unnormalized probabilities (in the sense that they may not sum to 1).
    """
    probabilities_by_distribution_value = OrderedDict()
    for token, logprob in top_logprobs.items():
        if token in VALID_ACTIVATION_TOKENS:
            token_as_int = int(token)
            probabilities_by_distribution_value[token_as_int] = np.exp(logprob)
    return probabilities_by_distribution_value

    Given a map from tokens to logprobs, return a map from distribution values (integers on the
    range [0, 10]) to unnormalized probabilities (in the sense that they may not sum to 1).
    
neuron_explainer/explanations/simulator.py