hi,thank u in advance

```
import pymc as pm
import numpy as np
import pandas as pd
import arviz as az
import matplotlib.pyplot as plt
import xarray as xr
import aesara
#data
trainSize,testSize = 1000,400
group_num_all = 7
group_num_train = 5
field_num = 3
group_size = (trainSize+testSize)/group_num_all
group_idx_val = np.repeat(range(group_num_all),group_size)
group_idx_train,group_idx_test = group_idx_val[:trainSize],group_idx_val[trainSize:]
x_train,x_test=np.random.random((trainSize,field_num)),np.random.random((testSize,field_num))
y_train,y_test=np.random.random((trainSize,1)),np.random.random((testSize,1))
#modeling
with pm.Model() as model:
slope_mu = pm.Normal("slope_mu", 0.5, 0.1)
slope_sigma = pm.Normal("slope_sigma", 0.5, 0.1)
intercept_mu = pm.Normal("intercept_mu", 0.5, 0.1)
intercept_sigma = pm.Normal("intercept_sigma", 0.5, 0.1)
group_num = pm.MutableData("group_num",group_num_train)
slope = pm.Normal("slope", slope_mu, slope_sigma,shape=(group_num,field_num))
#note key is here---,
# if shape=(group_num,) then sample is ok,but sample_prior_predictive fails
# if shape=(group_num,1) then sample fails,but sample_prior_predictive is ok
intercept = pm.Normal("intercept", intercept_mu, intercept_sigma,shape=(group_num,1))
group_idx = pm.MutableData("group_idx",group_idx_train)
x = pm.MutableData("x",x_train)
mu = intercept[group_idx] + pm.math.sum(x*slope[group_idx],axis=1)
sigma = pm.Exponential("sigma", 1.0)
pm.Normal("obs", mu=pm.Deterministic("y",mu), sigma=sigma, observed=y_train)
idata = pm.sample_prior_predictive(random_seed=100)
trace = pm.sample()
```

sample error:

ValueError: array is not broadcastable to correct shape

Apply node that caused the error: AdvancedIncSubtensor1{inplace,inc}(Elemwise{Composite{((-i0) / i1)}}.0, Elemwise{true_div,no_inplace}.0, group_idx)

Toposort index: 77

Inputs types: [TensorType(float64, (None, None)), TensorType(float64, (1000, None)), TensorType(int32, (None,))]

Inputs shapes: [(5, 1), (1000, 1000), (1000,)]

Inputs strides: [(8, 8), (8000, 8), (4,)]

sample_prior_predictive error:

ValueError: Output size (1000, 1) is not compatible with broadcast dimensions of inputs (1000, 1000).