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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 get_matrix(self, x1=None, x2=None, include_diagonal=None, include_general=None):
 """ Get the covariance matrix at given independent coordinates Args: x1 (Optional[array[n1]]):
 The first set of independent coordinates. If this is omitted, ``x1`` will be assumed to be equal to ``x`` from a previous call to :func:`GP.compute`. x2 (Optional[array[n2]]):
 The second set of independent coordinates. If this is omitted, ``x2`` will be assumed to be ``x1``. include_diagonal (Optional[bool]):
 Should the white noise and ``yerr`` terms be included on the diagonal? (default: ``False``) """

        if x1 is None and x2 is None:
            if self._t is None or not self.computed:
                raise RuntimeError("you must call 'compute' first")
            K = self.kernel.get_value(self._t[:, None] - self._t[None, :])
            if include_diagonal is None or include_diagonal:
                K[np.diag_indices_from(K)] += (
                    self._yerr**2 + self.kernel.jitter
                )
            if (include_general is None or include_general) and len(self._A):
                K[np.diag_indices_from(K)] += self._A
                K += np.tril(np.dot(self._U.T, self._V), -1)
                K += np.triu(np.dot(self._V.T, self._U), 1)
            return K

        incl = False
        x1 = np.ascontiguousarray(x1, dtype=float)
        if x2 is None:
            x2 = x1
            incl = include_diagonal is not None and include_diagonal
        K = self.kernel.get_value(x1[:, None] - x2[None, :])
        if incl:
            K[np.diag_indices_from(K)] += self.kernel.jitter
        return K