Dimension error for Hierarchical model

Questions version agnostic
4

updating code script as there are some bugs when I was anonymizing it.

import pymc as pm
import aesara
import aesara.tensor as at
import pandas as pd
import numpy as np

#generate some random data
num_channels= 10
num_cont_vars= 4
num_weeks= 24
num_regions= 5
target= 'Sales'

dict_1= {
    'Region': np.repeat(np.arange(num_regions), num_weeks ),
    'Week': np.repeat(np.arange(num_weeks), num_regions),
    'Sales': np.random.randint(low= 10, high= 100, size= num_weeks*num_regions),
}
dict_2= {f'Channel_{col}': np.random.randint(low=1, high=20, size=(num_weeks*num_regions)) for col in range(num_channels)}
dict_3= {f'cont_var{cont}': np.random.random(size= (num_weeks * num_regions)) for cont in range(num_cont_vars)}
dict_1.update(dict_2)
dict_1.update(dict_3)
df= pd.DataFrame(dict_1)

#defining adstock and saturation functions
def geometric_adstock(x, alpha: float, l: int):
    """Geometric adstock transformation."""
    cycles = [
        at.concatenate(
            [at.zeros(i), x[: x.shape[0] - i]]
        )
        for i in range(l)
    ]
    x_cycle = at.stack(cycles)
    w = at.as_tensor_variable([at.power(alpha, i) for i in range(l)])
    return at.dot(w, x_cycle)


def logistic_saturation(x, lam: float):
    """Logistic saturation transformation."""
    return (1 - at.exp(-lam * x)) / (1 + at.exp(-lam * x))


# Building the model:

coords= {'Region' : list(df['Region'].unique())}
with pm.Model(coords= coords) as model:

    #defining the hyperpriors
    adstock_a= pm.Exponential('adstock_a', lam=10.0)
    adstock_b= pm.Exponential('adstock_b', lam=10.0)
    sat_lam_lam= pm.Exponential('sat_lam', lam= 10.0)
    channel_beta_lam= pm.Exponential('coef_lam', lam=10.0)
    cont_beta_mu= pm.Normal('cont_lam', mu=0, sigma=10.0)
    cont_beta_sigma= pm.HalfCauchy('cont_sig', beta= 10.0)

    #then I loop over the marketing channels and calculate their priors and add them to the response list

    response_lst= []
    for channel in [channel for channel in df.columns if channel.startswith('Channel')]:
        x= df[channel].values
        adstock_param= pm.Beta(f'{channel}_adstock', alpha= adstock_a, beta= 
                adstock_b, dims= 'Region' )
        sat_lam= pm.Exponential(f'{channel}_sat',
                                        lam= sat_lam_lam,
                                        dims= 'Region') 
        channel_b = pm.Exponential(f"{channel}_coef",
                                    lam=channel_beta_lam,
                                    dims= 'Region')

        x_new = geometric_adstock(x, adstock_param, 6)            
        saturation_tensor = logistic_saturation(x_new, sat_lam )  

        channel_contribution= pm.Deterministic(
                f'contribution_{channel}',
                channel_b * saturation_tensor)  

        response_lst.append(channel_contribution)   

    for control_var in [var for var in df.columns if var.startswith('cont_var')]:
        x = df[control_var].values
        control_beta = pm.Normal(f"{control_var}_control_coef",
                                    mu=cont_beta_mu,
                                    sigma=cont_beta_sigma,
                                    dims= 'Region')
        control_x = control_beta * x
        response_lst.append(control_x)\


    intercept= pm.Exponential('intercept', lam= 0.1)   


    sigma = pm.Exponential('sigma', lam=0.01)

    likelihood = pm.Normal("outcome", mu = sum(response_lst) + intercept , sigma = sigma,
                            observed = df[target].values,
                            dims= 'Region'
                            )               

    trace= pm.sample(1000, tune= 500)