Hello,

I am trying to create a custom step function for my model so that I can include a variable without making said variable a free random variable. I could not adapt the solution provided in here for my model; running the notebook with PyMC 5.6.1 throws this error:

```
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/pymc/sampling/parallel.py", line 122, in run
self._start_loop()
File "/usr/local/lib/python3.8/dist-packages/pymc/sampling/parallel.py", line 174, in _start_loop
point, stats = self._step_method.step(self._point)
File "/usr/local/lib/python3.8/dist-packages/pymc/step_methods/compound.py", line 236, in step
sts.pop("model_logp", None)
AttributeError: 'tuple' object has no attribute 'pop'
"""
The above exception was the direct cause of the following exception:
AttributeError Traceback (most recent call last)
File /usr/local/lib/python3.8/dist-packages/pymc/sampling/parallel.py:122, in run()
121 self._point = self._make_numpy_refs()
--> 122 self._start_loop()
123 except KeyboardInterrupt:
File /usr/local/lib/python3.8/dist-packages/pymc/sampling/parallel.py:174, in _start_loop()
173 try:
--> 174 point, stats = self._step_method.step(self._point)
175 except SamplingError as e:
File /usr/local/lib/python3.8/dist-packages/pymc/step_methods/compound.py:236, in step()
235 for sts in stats[:-1]:
--> 236 sts.pop("model_logp", None)
237 return point, stats
AttributeError: 'tuple' object has no attribute 'pop'
```

I managed to write something that works based on this question. But there are two issue that I cannot figure out how to solve:

- The custom step values are not recorded in the inference data
- I would like to control the random seed in my custom step when using multiple cores

I am using PyMC version 5.6.1.

This is what I have so far:

```
import numpy as np
import pymc as pm
import arviz as az
import corner
########################
## generating test data
## not really important
def gauss_1d(x, mu, sig):
return np.exp(-np.square(x-mu)/sig)
def func(mass, Phi_s, Ms, a):
mvec = np.power(10, mass-Ms)
return Phi_s * np.power(mvec, a+1) * np.exp(-np.power(10,mvec))*1e4
nbin = 10
ndraw = 2 + nbin
mat = np.array([gauss_1d(np.arange(ndraw), i-1.1/(i+1), (i+1)/(3+i))*2/(ndraw-i) for i in range(ndraw)])
mat = mat/np.sum(mat, axis = 1, keepdims = True)*np.sin(np.linspace(np.pi/ndraw, np.pi/2, ndraw))[:,None]
mat = mat[1:ndraw-1,1:ndraw-1]
ml, mu = 8, 11
mbins = np.linspace(ml, mu, nbin+1)
mcens = 0.5*(mbins[1:]+mbins[:-1])
sel_par = [.01, 10.75, -1.1]
sel_mat = np.random.normal(mat, mat/10, mat.shape)
obs_val = np.sum(func(mcens, *sel_par)[:,None]*sel_mat, axis = 0, dtype = int)
###################
## custom step function
from pymc.step_methods.arraystep import BlockedStep
class FixedDistStep(BlockedStep):
def __init__(self, var, md, mu, seed=4):
model = pm.modelcontext(None)
value_var = model.rvs_to_values[var]
self.vars = [value_var]
self.name = value_var.name
self.rng = np.random.default_rng(seed=seed)
self.md = md
self.mu = mu
def step(self, point: dict):
mat = self.rng.normal(self.md, self.mu, self.md.shape)
mat[mat<0] = 0
mat[mat>1] = 1
point[self.name] = mat
return point, []
###################
## the model
with pm.Model() as rocky:
a = pm.Uniform('a', lower=-2, upper=-.5)
Ms = pm.Uniform('Ms', lower=8, upper=14)
Phi_s = pm.Uniform('Phi_s', lower=0.0, upper=.02)
mat_prc = pm.TruncatedNormal('mat_prc', mu=mat, sigma=(mat+1e-14)/10,
lower=0, upper=1, shape=mat.shape, transform=None)
phi_true = pm.Deterministic('phi_true', func(mcens, Phi_s, Ms, a))
obs_ns = pm.Deterministic('obs_ns', pm.math.sum(phi_true[:,None]*mat_prc, axis = 0))
logl_R = pm.Normal('rec_pars', mu=obs_ns, observed=obs_val,
sigma=np.power(obs_ns, .888))
fixed_step = [FixedDistStep(mat_prc, mat, mat/10)]
with rocky:
idata = pm.sample(draws = 4096, tune = 1024, chains = 4, step=fixed_step,
cores = 4, random_seed=np.random.default_rng(seed = 4))
```

In the inference data `mat_prc`

(the custom step variable) is a matrix of 0.5s for all the steps. The step is actually working and doing what it should, however, the values are not being recorded. How can I store the values correctly? And how can I assign a different numpy random seed to each chain?

I would appreciate any help. Thanks!

-Amir