How can I implement RandomizedSearchCV for GradientBoostingRegressor in scikit-learn instead of GridSearchCV?

I am trying to run a regression model using sklearn GradientBoostingRegressor. I have seen some GridSearchCV implementations for the hyperparameter tuning, however in order to reduce the computation time I would like to implement RandomizedSearch. Unfortunately I could not make these both run together. Could you please help me how to implement?

My script for GridSearchCV is below, I unfortunately could not manage it to convert to RandomizedSearchCV using gradient boosting estimator.

from sklearn.ensemble import GradientBoostingRegressor
from sklearn.model_selection import RandomizedSearchCV

print("Optimizing Hyperparameters..")
LR = {"learning_rate": [0.001],
      # "n_estimators": [10, 50, 100, 150, 500, 1000, 15000],
      "n_estimators": [1000, 3000, 5000, 7000, 10000],
      "max_depth": [1, 2, 3, 5, 7, 10]}
tuning = RandomizedSearchCV(estimator=GradientBoostingRegressor(), param_distributions=LR)
tuning.fit(X_train, y_train)

print("Best Parameters found: ", tuning.best_params_)

n_parameter = tuning.best_params_["n_estimators"]
lr_parameter = tuning.best_params_["learning_rate"]
md_parameter = tuning.best_params_["max_depth"]