Update: The error mentioned above is resolved by using init = 'adapt_diag" / āadviā as mentioned in some of the other posts, however the execution is still very slow even after implementing the non-centered parameterization, the sampling almost stalls after completing around 10%.

I checked by taking an even smaller subset of the data but its the same.

I am using the following model specification, is something really basic wrong with the code?

```
hbr_model = pm.Model()
with hbr_model:
posnormal = pm.Bound(pm.Normal,lower = 0.0)
negnormal = pm.Bound(pm.Normal,upper = 0.0)
#Hyperpriors for group nodes:-
mu_a = posnormal('mu_a',mu = 0.0,sd = 1e3)
sigma_a = pm.HalfNormal('sigma_a',10)
mu_b = posnormal('mu_b',mu = 0.0,sd = 1e3)
sigma_b = pm.HalfNormal('sigma_b',10)
mu_c = posnormal('mu_c',mu = 0.0,sd = 1e3)
sigma_c = pm.HalfNormal('sigma_c',10)
mu_d = posnormal('mu_d',mu = 0.0,sd = 1e3)
sigma_d = pm.HalfNormal('sigma_d',10)
a_offset = pm.Normal('a_offset',mu = 0.0,sd = 1,shape = asins_num)
a = pm.Deterministic('a',mu_a + a_offset * sigma_a)
b_offset = pm.Normal('b_offset',mu = 0.0,sd = 1,shape = asins_num)
b = pm.Deterministic('b',mu_b + b_offset * sigma_b)
c_offset = pm.Normal('c_offset',mu = 0.0,sd = 1,shape = asins_num)
c = pm.Deterministic('c',mu_c + c_offset * sigma_c)
d_offset = pm.Normal('d_offset',mu = 0.0,sd = 1,shape = asins_num)
d = pm.Deterministic('d',mu_d + d_offset * sigma_d)
sigma_y = pm.HalfNormal('sigma_y',sigma = 1)
mu = a[asin_idx] + b[asin_idx] * x['Discount'] + c[asin_idx] * x['SB_Impressions'] + d[asin_idx] * x['SNB_Impressions']
Y = pm.Normal('Y',mu = mu,sigma = sigma_y,observed = Y_Act)
```

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