Combine pymc3 with a scikit-learn object

Hi, I’m using pymc3 to optimize two parameters in my GaussianProcessRegressor which is a scikit-learn object. And I tried many methods but it still can’t run. So I came here for some suggestion.
Below is my code:

X = data_1[‘param’] #which is 88×400
Cp_E = data_1[‘Cp_E’] #1×400
Y = data_1[‘Cp_M’] #88×400
gaussian=GaussianProcessRegressor(), Y)

def main(argv=None):
  with pm.Model() as model_:
    Cdt1 = pm.Normal(‘Cdt1’, mu = 20., sd = 10.)
    CDESkeps = pm.Normal(‘CDESkeps’, mu = 0.6, sd = 3.)
    epsilon = pm.Uniform(‘epsilon’, lower = 0, upper = 1)
    Z = np.array([Cdt1, CDESkeps])
    YX=gaussian.predict(Z.reshape(1, -1))
    y_pred = pm.Normal(‘y_pred’, mu = YX, sd = epsilon, observed = Cp_E)
    start = pm.find_MAP()
    step = pm.NUTS(scaling = start)
    trace_ = pm.sample(5000, step = step, start = start)
  parameters = [‘Cdt1’, ‘CDESkeps’]
  pm.traceplot(trace_, parameters)

if name==‘main’:

The error is setting an array element with a sequence. But I have confirmed that both the shape of YX and Cp_E are (1,400).
Hope someone can give me some suggestion, thanks in advance.

I am confused by the code.
Cdt1, CDESkeps are the two parameters, but they are treated as input in YX=gaussian.predict(Z.reshape(1, -1)).

Can we see the actual error message?