gp models predictive_gradients with mean function

Hi,

I found that when calling predictive_gradients for a gp model with non trivial mean, the gradient returned for mean does not include the contribute of gradient from the mean function. To fix it, I believe simply add the gradient of mean to the result is correct. Here is an example where the I use linear mean function, and gradient of predicted means are about 0.

import numpy as np
import GPy

x_test = np.linspace(-1,1,10).reshape(-1,1)
mf = GPy.mappings.Linear(1,1)
X = np.linspace(-1,1,10).reshape(-1,1)
Y = X
gp_model = GPy.models.GPRegression(X, Y, mean_function=mf)
gp_model.optimize_restarts()
print(gp_model)
print(gp_model.predict(x_test)[0])
print(gp_model.predictive_gradients(x_test)[0])

Start by running the provided Python example and tracing the GP model's predictive_gradients path. Check how the mean function is handled when computing gradients; done means the returned predicted-mean gradients include the contribution from the non-trivial linear mean function and the example no longer reports values near zero.