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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 _error_and_gradient(self, x):
 """Compute the error and the gradient. This is the function optimized by :obj:`scipy.optimize.minimize`. Args: x (`array-like`):
 [`m` * `n`, ] matrix. Returns: `tuple`: containing: - Error (`float`) - Gradient (`np.array`) [`m`, `n`] """

        coords = x.reshape((self.m, self.n))
        d = squareform(pdist(coords))
        diff = self.D - d
        error = self._error(diff)
        gradient = self._gradient(diff, d, coords)
        return error, gradient.ravel()