<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def predict(self, y, t=None, return_cov=True, return_var=False):
 """ Compute the conditional predictive distribution of the model You must call :func:`GP.compute` before this method. Args: y (array[n]):
 The observations at coordinates ``x`` from :func:`GP.compute`. t (Optional[array[ntest]]):
 The independent coordinates where the prediction should be made. If this is omitted the coordinates will be assumed to be ``x`` from :func:`GP.compute` and an efficient method will be used to compute the prediction. return_cov (Optional[bool]):
 If ``True``, the full covariance matrix is computed and returned. Otherwise, only the mean prediction is computed. (default: ``True``) return_var (Optional[bool]):
 If ``True``, only return the diagonal of the predictive covariance; this will be faster to compute than the full covariance matrix. This overrides ``return_cov`` so, if both are set to ``True``, only the diagonal is computed. (default: ``False``) Returns: ``mu``, ``(mu, cov)``, or ``(mu, var)`` depending on the values of ``return_cov`` and ``return_var``. These output values are: (a) **mu** ``(ntest,)``: mean of the predictive distribution, (b) **cov** ``(ntest, ntest)``: the predictive covariance matrix, and (c) **var** ``(ntest,)``: the diagonal elements of ``cov``. Raises: ValueError: For mismatched dimensions. """

        y = self._process_input(y)
        if len(y.shape) > 1:
            raise ValueError("dimension mismatch")

        if t is None:
            xs = self._t
        else:
            xs = np.ascontiguousarray(t, dtype=float)
            if len(xs.shape) > 1:
                raise ValueError("dimension mismatch")

        # Make sure that the model is computed
        self._recompute()

        # Compute the predictive mean.
        resid = y - self.mean.get_value(self._t)

        if t is None:
            alpha = self.solver.solve(resid).flatten()
            alpha = resid - (self._yerr**2 + self.kernel.jitter) * alpha
        elif not len(self._A):
            alpha = self.solver.predict(resid, xs)
        else:
            Kxs = self.get_matrix(xs, self._t)
            alpha = np.dot(Kxs, alpha)

        mu = self.mean.get_value(xs) + alpha
        if not (return_var or return_cov):
            return mu

        # Predictive variance.
        Kxs = self.get_matrix(xs, self._t)
        KxsT = np.ascontiguousarray(Kxs.T, dtype=np.float64)
        if return_var:
            var = -np.sum(KxsT*self.apply_inverse(KxsT), axis=0)
            var += self.kernel.get_value(0.0)
            return mu, var

        # Predictive covariance
        cov = self.kernel.get_value(xs[:, None] - xs[None, :])
        cov -= np.dot(Kxs, self.apply_inverse(KxsT))
        return mu, cov