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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 _set_oversampling(self, value):
 """ This is a private method because it's used in the initializer by the ``oversampling`` """


        try:
            value = np.atleast_1d(value).astype(float)
            if len(value) == 1:
                value = np.repeat(value, 2)
        except ValueError:
            raise ValueError('Oversampling factors must be float')
        if np.any(value <= 0):
            raise ValueError('Oversampling factors must be greater than 0')

        self._oversampling = value