Hi all,

Can you please tell me how to write the code when missing values are included in y when sampling with numpyro?

Example code:

```
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import arviz as az
from sklearn.datasets import make_regression
import pymc as pm
import pytensor.tensor as pt
# Initialize random number generator
RANDOM_SEED = 8927
rng = np.random.default_rng(RANDOM_SEED)
az.style.use("arviz-darkgrid")
X, Y, true_coef = make_regression(n_samples=50, n_features=5, noise=1, random_state=42, coef=True, n_targets=2)
print(true_coef)
Y[0:10,1] = np.nan
X = pd.DataFrame(X, columns=[f"x{i}" for i in range(X.shape[1])])
Y = pd.DataFrame(Y, columns=[f"y{i}" for i in range(Y.shape[1])])
with pm.Model() as model:
# coords
model.add_coord('N', X.index, mutable=True)
model.add_coord('D_X', X.columns, mutable=False)
model.add_coord('D_Y', Y.columns, mutable=False)
# define prior
sigma = pm.HalfCauchy("sigma", beta=10, dims="N")
intercept = pm.Normal("intercept", mu=0, sigma=20, dims="D_Y")
slope = pm.Normal("slope", mu=0, sigma=20, dims=("D_X", "D_Y"))
# calc mu
mu = pm.Deterministic("mu", intercept + pt.dot(X, slope), dims=("N","D_Y" ))
# likelihood
likelihood = pm.Normal("likelihood", mu=mu, sigma=sigma, observed=Y)
with model:
idata = pm.sample(1000,
tune=500,
chains=3,
idata_kwargs={"log_likelihood": True},
random_seed=42,
return_inferencedata=True,
nuts_sampler="numpyro")
```

```
NotImplementedError: JAX does not support resizing arrays with boolean
masks. In some cases, however, it is possible to re-express your model
in a form that JAX can compile:
>>> import pytensor.tensor as pt
>>> x_pt = pt.vector('x')
>>> y_pt = x_pt[x_pt > 0].sum()
can be re-expressed as:
>>> import pytensor.tensor as pt
>>> x_pt = pt.vector('x')
>>> y_pt = pt.where(x_pt > 0, x_pt, 0).sum()
```

I would be thankful if you could help me with this question.