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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 sample(self, mu):
 """ Return random samples from this Gamma distribution. Parameters mu : array-like of shape n_samples or shape (n_simulations, n_samples) expected values Returns ------- random_samples : np.array of same shape as mu """

        # in numpy.random.gamma, `shape` is the parameter sometimes denoted by
        # `k` that corresponds to `nu` in S. Wood (2006) Table 2.1
        shape = 1. / self.scale
        # in numpy.random.gamma, `scale` is the parameter sometimes denoted by
        # `theta` that corresponds to mu / nu in S. Wood (2006) Table 2.1
        scale = mu / shape
        return np.random.gamma(shape=shape, scale=scale, size=None)