Is it possible to calculate prediction intervals from a `tidymodels`

stacked model?

Working through the example from the `stacks()`

package here yields the stacked frog model (which can be downloaded here for reprex) and the testing data:

```
data("tree_frogs")
tree_frogs <- tree_frogs %>%
filter(!is.na(latency)) %>%
select(-c(clutch, hatched))
set.seed(1)
tree_frogs_split <- initial_split(tree_frogs)
tree_frogs_train <- training(tree_frogs_split)
tree_frogs_test <- testing(tree_frogs_split)
```

I tried to run something like this:

`pi <- predict(tree_frogs_model_st, tree_frogs_test, type = "pred_int")`

but this gives an error:

`Error in UseMethod("stack_predict") : no applicable method for 'stack_predict' applied to an object of class "NULL"`

Reading the documentation of `stacks()`

I also tried passing "pred_int" in the `opts`

list:

`pi <- predict(tree_frogs_model_st, tree_frogs_test, opts = list(type = "pred_int"))`

but this just gives: `opts is only used with type = raw and was ignored. `

For reference, I am trying to do a similar thing that is done in Ch.19 of Tidy Modeling with R book

```
lm_fit <- fit(lm_wflow, data = Chicago_train)
predict(lm_fit, Chicago_test, type = "pred_int")
```

which seems to work fine for a single model fit like `lm_fit`

, but apparently not for a stacked model?

Am I missing something? Is it not possible to calculate prediction intervals for stacked models for some reason?