Can not use time series parameters in DifferentialEquation

Questions v5
1

I am trying to calibrate the two rate constant (tauep and tauet) of my simple Enzyme model
dE/dt = tauep*B - tauet*E

while using time series data of B, it shows error. The whole code is given below
"

def enz_f(E, t, p):
    B_t = np.interp(t, times, B_series) 
    return ((p[0] * B_t) - (p[1] * E[0]))
  
times = np.arange(0, 5, 1)

B_series = [125, 126, 127, 128, 129]

E0 = 150
sigma_obs = 2

true_params = [0.00012, 0.024]
tauep_true = 0.00012
tauet_true = 0.024

y_true = odeint(enz_f, [E0], t=times, args=(true_params,))

# Adding noise to the true values to simulate observations
yobs = np.random.normal(y_true, sigma_obs)

ode_model = DifferentialEquation(
    func= enz_f,  
    times=times, 
    n_states=1, 
    n_theta=3,  
    t0=0
)

with pm.Model() as model:
    # Priors for model parameters
    sigma = pm.HalfCauchy("sigma", beta=1)
    tauep = pm.LogNormal("tauep", mu=np.log(tauep_true), sigma=0.5)
    tauet = pm.LogNormal("tauet", mu=np.log(tauet_true), sigma=0.5)

    # Solve the ODE using the priors, with dynamic B
    ode_solution = ode_model(y0=[E0], theta=[tauep, tauet])

    # Define the likelihood function
    Y = pm.Normal("Y", mu=ode_solution, sigma=sigma, observed=yobs)

    # Sample from the prior
    prior = pm.sample_prior_predictive()

    # Sample from the posterior
    trace = pm.sample(500, tune=1000, cores=3)

    # Sample from the posterior predictive distribution
    posterior_predictive = pm.sample_posterior_predictive(trace)

az.plot_trace(trace)
plt.show()

"
Can anyone suggest me where I am doing wrong? or is there a better way to do it?