Discretization of Continuous Variables in Pymc

Questions v5
15 
import pytensor.tensor as pt
import numpy as np
import pymc as pm

file_path = 'Combination.xlsx'
df = pd.read_excel(file_path, sheet_name = 0, header=None)  
Combination_cpt = df.to_numpy().T
Combination_cpt.shape
bins = np.array([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120])

# num_states_plf = len(bins) - 1 
# num_states_slf = len(bins) - 1

Combination_cpt_tensor = pt.as_tensor_variable(Combination_cpt)

with pm.Model() as model:
    PoA = pm.Triangular('PoA', lower=0, c=0.3, upper=1)
    CF = pm.Lognormal('CF', mu=4.5, sigma=0.2)
    TEF = pm.Deterministic('TEF', CF * PoA)
    vulnerability = pm.Normal('vulnerability', mu=1, sigma=0.5)
    MPLEF = pm.Deterministic('MPLEF', TEF * vulnerability)
    CoSL = pm.Normal('CoSL', mu=0.7, sigma=0.2)
    PLF = pm.Poisson('PLF', mu=MPLEF)
    SLF = pm.Binomial('SLF', n=PLF, p=CoSL)

    plf_bin = pt.searchsorted(bins, PLF) - 1
    slf_bin = pt.searchsorted(bins, SLF) - 1 
    
    def get_cpt_value_combination(plf_state, slf_state, cpt_tensor):
        idx = plf_state * 12 + slf_state
        return cpt_tensor[idx]

    p_Combination = get_cpt_value_combination(plf_bin, slf_bin, Combination_cpt_tensor)
    Combination = pm.Categorical('Combination', p=p_Combination)
    
    trace = pm.sample(10000, tune=1000)

Thank you for your suggestions!! I tried converting the CPTs into tensors in advance and also followed your advice. However, sampling from the Combination node is still not working. Are there any other issues that could be causing this?