Using Laplace as likelihood distribution

Questions
3

Here is the code:

def S2_0_Bayesian_Interface(data):
########################################################################################################################
    with pm.Model() as model_0:
            # Prior Distributions for unknown model parameters:
            b_0 = pm.HalfNormal('b_0', sd=5)
            mu_0 = pm.Normal('mu_0', mu=0, sd=5)

            # Observed data is from a Likelihood distributions (Likelihood (sampling distribution) of observations):
            observed_data_0 = Laplace('observed_data_0', mu=mu_0, b=b_0, observed=data)

            # Printing the result of log_likelihood:
            # print('log_likelihood result:', model_0)

            # draw 5000 posterior samples
            trace_0 = pm.sample(draws=1000, tune=1000, chains=3, cores=1, progressbar=True)
    
            # Obtaining Posterior Predictive Sampling:
            post_pred_0 = pm.sample_posterior_predictive(trace_0, samples=1000)
            print(post_pred_0['observed_data_0'].shape)
            print('\nSummary: ')
            print(pm.stats.summary(data=trace_0))
            print(pm.stats.summary(data=post_pred_0))
########################################################################################################################
    return trace_0, post_pred_0

but when I ran the code, it says that it does not have observed parameter