@staticmethod<EOL><INDENT>def _get_best(values: List[float], losses: List[float],<EOL>max_loss_div: float = <NUM_LIT>, min_val_div: float = <NUM_LIT>) -> float:<DEDENT>
assert len(values) == len(losses), "<STR_LIT>"<EOL>min_ind = np.argmin(losses)<EOL>for i in range(min_ind - <NUM_LIT:1>, <NUM_LIT:0>, -<NUM_LIT:1>):<EOL><INDENT>if (losses[i] * max_loss_div > losses[min_ind]) or(values[i] * min_val_div < values[min_ind]):<EOL><INDENT>return values[i + <NUM_LIT:1>]<EOL><DEDENT><DEDENT>return values[min_ind] / min_val_div<EOL>
Find the best value according to given losses

Args:
    values: list of considered values
    losses: list of obtained loss values corresponding to `values`
    max_loss_div: maximal divergence of loss to be considered significant
    min_val_div: minimum divergence of loss to be considered significant

Returns:
    best value divided by `min_val_div`