Hello dear Bayesians!

I was trying to reproduce models from some of the model_builder tests, and I’ve encountered this situation:

```
with pm.Model() as model:
x = pm.MutableData('x', data['input'].values)
y_data = pm.MutableData('y_data', data['output'].values)
a = pm.Normal('a', mu = 0, sigma = 10)
b = pm.Normal('b', mu = 0, sigma = 10)
obs_error = pm.HalfNormal('σ_model_fmc', sigma = 2)
result = pm.Normal('y_model', mu = a+b*x, sigma = obs_error, shape = x.shape, observed = y_data)
idata = pm.sample()
```

After this I assigned new x’s

```
x_pred = [1,2,3,4,5]
with model:
pm.set_data({'x' : x_pred})
```

But quite fast I’ve realized that the test has different data structure passed as x, so I fixed it to reproduce it 1:1. So after quick fix my new x assignment looked like that:

```
x_pred2 = np.random.uniform(low=0, high=1, size=100)
prediction_data = pd.DataFrame({'input' : x_pred2})
with model:
pm.set_data({'x' : prediction_data.input.values})
```

Which went all right, but when I tried to get predictions:

```
with model:
idata = pm.sample_posterior_predictive(idata, predictions = True, extend_inferencedata=True)
```

I was given this error:

Is this desired behavior, that I can’t assign new values to the mutable data container without rebuilding the model? If I’d pass something of shape of the first x_pred, it works fine, otherwise it throws error