def _dK_computations(self, dL_dK):
self._dL_dl = (dL_dK*self.variance*self._K_dvar*(self.input_dim/<NUM_LIT>*(self._lengthscales_two.T**<NUM_LIT:4> - self._lengthscales**<NUM_LIT:4>) + <NUM_LIT:2>*self._lengthscales2*self._K_dist2)/(self._w2*self._w2*self._lengthscales)).sum(<NUM_LIT:1>)<EOL>if self._lengthscales_two is self._lengthscales:<EOL><INDENT>self._dL_dl_two = None<EOL><DEDENT>else:<EOL><INDENT>self._dL_dl_two = (dL_dK*self.variance*self._K_dvar*(self.input_dim/<NUM_LIT>*(self._lengthscales**<NUM_LIT:4> - self._lengthscales_two.T**<NUM_LIT:4> ) + <NUM_LIT:2>*self._lengthscales_two2.T*self._K_dist2)/(self._w2*self._w2*self._lengthscales_two.T)).sum(<NUM_LIT:0>)<EOL><DEDENT>
Pre-computations for the gradients of the covaraince function. Here the gradient of the covariance with respect to all the individual lengthscales is computed.
        :param dL_dK: the gradient of the objective with respect to the covariance function.
        :type dL_dK: ndarray