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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 LMLgrad(self,params=None):
 """ evaluates the gradient of the log marginal likelihood for the given hyperparameters """

        if params is not None:
            self.setParams(params)
        KV = self._update_cache()
        W = KV['W']
        LMLgrad = SP.zeros(self.covar.n_params)
        for i in range(self.covar.n_params):
            Kd = self.covar.Kgrad_param(i)
            LMLgrad[i] = 0.5 * (W*Kd).sum()
        return {'covar':LMLgrad}