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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 _init_variational_parameters(self, num_inducing, q_mu, q_sqrt, q_diag):
 """ Constructs the mean and cholesky of the covariance of the variational Gaussian posterior. If a user passes values for `q_mu` and `q_sqrt` the routine checks if they have consistent and correct shapes. If a user does not specify any values for `q_mu` and `q_sqrt`, the routine initializes them, their shape depends on `num_inducing` and `q_diag`. Note: most often the comments refer to the number of observations (=output dimensions) with P, number of latent GPs with L, and number of inducing points M. Typically P equals L, but when certain multioutput kernels are used, this can change. Parameters :param num_inducing: int Number of inducing variables, typically refered to as M. :param q_mu: np.array or None Mean of the variational Gaussian posterior. If None the function will initialise the mean with zeros. If not None, the shape of `q_mu` is checked. :param q_sqrt: np.array or None Cholesky of the covariance of the variational Gaussian posterior. If None the function will initialise `q_sqrt` with identity matrix. If not None, the shape of `q_sqrt` is checked, depending on `q_diag`. :param q_diag: bool Used to check if `q_mu` and `q_sqrt` have the correct shape or to construct them with the correct shape. If `q_diag` is true, `q_sqrt` is two dimensional and only holds the square root of the covariance diagonal elements. If False, `q_sqrt` is three dimensional. """

        q_mu = np.zeros((num_inducing, self.num_latent)) if q_mu is None else q_mu
        self.q_mu = Parameter(q_mu, dtype=settings.float_type)  # M x P

        if q_sqrt is None:
            if self.q_diag:
                self.q_sqrt = Parameter(np.ones((num_inducing, self.num_latent), dtype=settings.float_type),
                                        transform=transforms.positive)  # M x P
            else:
                q_sqrt = np.array([np.eye(num_inducing, dtype=settings.float_type) for _ in range(self.num_latent)])
                self.q_sqrt = Parameter(q_sqrt, transform=transforms.LowerTriangular(num_inducing, self.num_latent))  # P x M x M
        else:
            if q_diag:
                assert q_sqrt.ndim == 2
                self.num_latent = q_sqrt.shape[1]
                self.q_sqrt = Parameter(q_sqrt, transform=transforms.positive)  # M x L/P
            else:
                assert q_sqrt.ndim == 3
                self.num_latent = q_sqrt.shape[0]
                num_inducing = q_sqrt.shape[1]
                self.q_sqrt = Parameter(q_sqrt, transform=transforms.LowerTriangular(num_inducing, self.num_latent))