I want to tune a ridge regression using `tidymodels`

. I have looked at this nested sampling tutorial, but not sure how to increase the tuning from one to two hyperparameters. Please see example below:

Example data:

```
library("mlbench")
sim_data <- function(n) {
tmp <- mlbench.friedman1(n, sd = 1)
tmp <- cbind(tmp$x, tmp$y)
tmp <- as.data.frame(tmp)
names(tmp)[ncol(tmp)] <- "y"
tmp
}
set.seed(9815)
train_dat <- sim_data(50)
```

Setting inner and outer folds:

```
library(tidymodels)
results_nested_resampling <- rsample::nested_cv(train_dat,
outside = vfold_cv(v=10, repeats = 1),
inside = vfold_cv(v=10, repeats = 1))
```

Function to fit the model and compute the RMSE works:

```
svm_rmse <- function(object, penalty = 1, mixture = 1) {
y_col <- ncol(object$data)
mod <-
parsnip::linear_reg(penalty = penalty, mixture = mixture) %>% # tune() uses the grid
parsnip::set_engine("glmnet") %>%
fit(y ~ ., data = analysis(object))
holdout_pred <-
predict(mod, assessment(object) %>% dplyr::select(-y)) %>%
bind_cols(assessment(object) %>% dplyr::select(y))
rmse(holdout_pred, truth = y, estimate = .pred)$.estimate
}
# In some case, we want to parameterize the function over the tuning parameter:
rmse_wrapper <- function(penalty, mixture, object) svm_rmse(object, penalty, mixture)
# testing rmse_wrapper
rmse_wrapper(penalty=0.1, mixture=0.1, object=results_nested_resampling$inner_resamples[[5]]$splits[[1]])
```

But function to tune over the two hyperparameters does not work:

```
tune_over_cost <- function(object) {
glmn_grid <- base::expand.grid(
penalty = 10^seq(-3, -1, length = 20),
mixture = (0:5) / 5)
df3_glmn_grid %>%
mutate(RMSE = map_dbl(glmn_grid$penalty, glmn_grid$mixture, rmse_wrapper, object = object))
}
tune_over_cost(object=results_nested_resampling$inner_resamples[[5]])
```

Thanks in advance.