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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, size=None):
 """ Sample from the prior distribution over datasets Args: size (Optional[int]):
 The number of samples to draw. Returns: array[n] or array[size, n]: The samples from the prior distribution over datasets. """

        self._recompute()
        if size is None:
            n = np.random.randn(len(self._t))
        else:
            n = np.random.randn(len(self._t), size)
        n = self.solver.dot_L(n)
        if size is None:
            return self.mean.get_value(self._t) + n[:, 0]
        return self.mean.get_value(self._t)[None, :] + n.T