"all models failed" in tidymodels

Question

I keep getting the following error when attempting to tune an xgboost model for multiclass classification with 7 different classes in tidymodels using the tune_bayes() function:

Error in `estimate_tune_results()`:
! All of the models failed. See the .notes column.
Run `rlang::last_error()` to see where the error occurred.
Warning message:
All models failed. See the `.notes` column.

Code:

set.seed(123)
swing_split <- initial_split(swing_data, strata = outcome)
swing_train <- training(swing_split)
swing_test <- testing(swing_split)
head(swing_train, n = 15)

 A tibble: 15 x 7
   outcome plate_x plate_z pfx_x pfx_z release_speed stand
   <fct>     <dbl>   <dbl> <dbl> <dbl>         <dbl> <fct>
 1 foul       0.19    1.89  0.36 -0.31          85.5 R    
 2 foul      -0.16    2.95 -0.73  1.52          92.6 L    
 3 foul       0.43    2.1   0.99 -1.64          79.6 L    
 4 foul      -0.35    2.74 -0.61  1.51          91.7 R    
 5 foul      -1.09    2.69 -1.2   1.46          93.1 R    
 6 foul       0.86    2.8   1.09  0.29          79.9 L    
 7 foul      -0.33    2.59  1.22  1.2           95.7 L    
 8 foul      -0.39    2.31 -0.12 -0.46          78.5 R    
 9 foul       0.17    1.77  1.53  0.59          85.9 R    
10 foul       0.17    3.54 -0.54  1.58          96.2 R    
11 foul      -0.69    3.01  0.96  1.57          94.7 L    
12 foul       0.82    1.97  1.07 -0.62          79.7 L    
13 foul       0.89    2.87  1.5   0.61          92.3 L    
14 foul      -0.07    1.5   1.46  0.55          81.8 L    
15 foul      -0.09    3.63 -0.57  1.37          91.1 L


set.seed(234)
swing_folds <- vfold_cv(swing_train, strata = outcome)

swing_rec <-
  recipe(outcome ~ ., data = swing_train) %>%
  step_dummy(stand, one_hot = T)

xgb_spec <- boost_tree(
  trees = 175, 
  tree_depth = tune(), min_n = tune(), 
  loss_reduction = tune(),                     
  sample_size = tune(), mtry = finalize(mtry(), swing_train),         
  learn_rate = 0.1) %>% 
  set_engine("xgboost", objective = "multi:softprob", num_class = 7) %>% 
  set_mode("classification")
swing_wf <- workflow(swing_rec, xgb_spec)

xgb_set <- extract_parameter_set_dials(swing_wf)

doParallel::registerDoParallel()
set.seed(345)
xgb_tune <- swing_wf %>% 
  tune_bayes(
    iter = 25,
    resamples = swing_folds, 
    param_info = xgb_set,
    metrics = metric_set(accuracy), 
    initial = 10,
    control = control_bayes(parallel_over = "everything", save_pred = T, verbose = T))

I have looked on the internet and tried a couple of things, but I continue to get that error.