I am noticing some strange behaviour when trying to use `aesara.scan`

with uniform random variables. For example consider this contrived example to randomly add one or two to the previous value in a sequence. (Note I seemed to need the `at.ones_like`

to avoid in the switch statement to avoid a `AttributeError: 'float' object has no attribute 'type'`

error)

```
def func(switcher, prev):
offset = pm.math.switch(
pm.math.lt(switcher, 0.5),
at.ones_like(prev),
2 * at.ones_like(prev),
)
return prev + offset
with pm.Model() as model:
switcher = pm.Uniform("switcher", lower=0, upper=1, shape=2)
result, updates = aesara.scan(
fn=func,
outputs_info={"initial": at.ones_like(1.0, dtype="float64")},
sequences=[switcher],
n_steps=2,
)
pm.Normal("output", mu=result, sigma=0.01)
trace = pm.sample()
az.plot_trace(trace)
```

Why has the distribution over `switcher`

been altered to be either between 0 and 0.5 or 0.5 to 1?? If I comment out the `pm.Normal(...)`

line I get the expected distribution from 0 to 1…what is `scan`

doing to meddle with this?