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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, n=1):
 """ Returns one or more samples from this probability distribution. :param int n: Number of samples to return. :return numpy.ndarray: An array containing samples from the distribution of shape ``(n, d)``, where ``d`` is the number of random variables. """

        samples = np.empty((n, self.n_rvs))
        idxs_to_sample = np.arange(n)

        iters = 0

        while idxs_to_sample.size and iters < self._maxiters:
            samples[idxs_to_sample] = self._dist.sample(len(idxs_to_sample))

            idxs_to_sample = idxs_to_sample[np.nonzero(np.logical_not(
                self._model.are_models_valid(samples[idxs_to_sample, :])
            ))[0]]

            iters += 1

        if idxs_to_sample.size:
            raise RuntimeError("Did not successfully postselect within {} iterations.".format(self._maxiters))

        return samples