如何通过使用tidymodel的knn模型获得具有多个结果的预测？

install.packages("tidymodels")
library(tidymodels)

data(iris)
set.seed(123)
train_indices <- sample(1:nrow(iris), 0.7 * nrow(iris))
train_data <- iris[train_indices, ]
test_data <- iris[-train_indices, ]

knn_model <- nearest_neighbor(weight_func = "rectangular", neighbors = 3) %>%
  set_engine("kknn") %>%
  set_mode("regression")

preprocess_pipeline <- recipe(Sepal.Length ~ ., data = train_data) %>%
  step_normalize(all_predictors()) %>%
  step_dummy(all_nominal()) %>%
  prep()

trained_model <- workflow() %>%
  add_recipe(preprocess_pipeline) %>%
  add_model(knn_model) %>%
  fit(data = train_data)

predictions <- predict(trained_model, new_data = test_data, type = "prob", neighbors = 3)