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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def robust_mean(log_values):
 """ Mean of values falling within the 25-75 percentiles. Parameters log_values : 2-d numpy.array Center is computed along the second axis (i.e. per row). Returns ------- center : numpy.array of length log_values.shape[1] """

    if log_values.shape[1] <= 3:
        # Too few values to use robust mean.
        return numpy.nanmean(log_values, axis=1)
    without_nans = numpy.nan_to_num(log_values)  # replace nan with 0
    mask = (
        (~numpy.isnan(log_values)) &
        (without_nans <= numpy.nanpercentile(log_values, 75, axis=1).reshape((-1, 1))) &
        (without_nans >= numpy.nanpercentile(log_values, 25, axis=1).reshape((-1, 1))))
    return (without_nans * mask.astype(float)).sum(1) / mask.sum(1)