<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def predict(self, log2_bayes_factors, reset_index=False):
 """Guess the most likely modality for each event For each event that has at least one non-NA value, if no modalilites have logsumexp'd logliks greater than the log Bayes factor threshold, then they are assigned the 'multimodal' modality, because we cannot reject the null hypothesis that these did not come from the uniform distribution. Parameters log2_bayes_factors : pandas.DataFrame A (4, n_events) dataframe with bayes factors for the Psi~1, Psi~0, bimodal, and middle modalities. If an event has no bayes factors for any of those modalities, it is ignored reset_index : bool If True, remove the first level of the index from the dataframe. Useful if you are using this function to apply to a grouped dataframe where the first level is something other than the modality, e.g. the celltype Returns ------- modalities : pandas.Series A (n_events,) series with the most likely modality for each event """

        if reset_index:
            x = log2_bayes_factors.reset_index(level=0, drop=True)
        else:
            x = log2_bayes_factors
        if isinstance(x, pd.DataFrame):
            not_na = (x.notnull() > 0).any()
            not_na_columns = not_na[not_na].index
            x.ix[NULL_MODEL, not_na_columns] = self.logbf_thresh
        elif isinstance(x, pd.Series):
            x[NULL_MODEL] = self.logbf_thresh
        return x.idxmax()