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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 _tukey(self,x,threshold):
 """ Count outliers according to Tukey's rule. Where Q1 is the lower quartile and Q3 is the upper quartile, an outlier is an observation outside of the range: [Q1 - k(Q3 - Q1), Q3 + k(Q3 - Q1)] k = 1.5 indicates an outlier k = 3.0 indicates an outlier that is "far out" """

        vals = x.values[~np.isnan(x.values)]
        try:
            q1, q3 = np.percentile(vals, [25, 75])
            iqr = q3 - q1
            low_bound = q1 - (iqr * threshold)
            high_bound = q3 + (iqr * threshold)
            outliers = np.where((vals > high_bound) | (vals < low_bound))
        except:
            outliers = []
        return outliers