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Given the following text problem statement, write Python code to implement the functionality described below in problem statement
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Problem:
Sklearn RandomizedSearchCV - Tuning Hyperparameters Using Randomized Search Cross Validation

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Python Code::
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV
# Step 1: Initialise Random Forest Classifier
rf = RandomForestClassifier(class_weight='balanced',
                            n_jobs=-1,
                            random_state=101)
# Step 2: Create a dictionary containing the parameters we want
# to tune and the values we want to sample from
params = {'n_estimators':list(range(1,100,10)),
         'max_depth':[None,5,10]}
# Step 3: Initialise RandomizedSearchCV with n_iter(the number of parameter settings to sample)
# set to 10 and then fit model before printing out the best parameters
model = RandomizedSearchCV(estimator=rf, 
                           param_distributions=params, 
                           n_iter=10,
                           random_state=101)
model.fit(X_train, y_train)
print(model.best_params_)

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