How to save XGBoost model trained using Python sklearn for easy portability to Java?

I am using RandomizedSearchCV to train an XGBoost model using the SKLearn API for XGBoost.

After training this model, how could I export it into the XGBoost internal format?
https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBRegressor.save_model

I need to then give this model to a team which uses the Java XGBoost library and are importing the model using this:

Booster booster = XGBoost.loadModel("model.bin");

Below is the RandomizedSearchCV I have with sklearn Python. The pipelines and hypeparameters currently hold only the XGBoost algorithm

pipelines = {
    'xgb' : make_pipeline(xgb.XGBClassifier(random_state=777)),
}

xgb_hyperparameters = {
    'xgbclassifier__objective': ['binary:logistic'],
    'xgbclassifier__n_estimators': [100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600],
    'xgbclassifier__learning_rate': [0.01, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ],
    'xgbclassifier__max_depth': [ 3, 4, 5, 6, 8,9, 10,11, 12,13,14, 15,16,17,18,19,20,25,30],
    'xgbclassifier__min_child_weight': [ 1, 3, 4, 5, 6, 7, 8, 9],
    'xgbclassifier__gamma': [ 0.0, 0.1, 0.2 , 0.3, 0.4, 0.5 ]
}


hyperparameters = {
    'xgb' : xgb_hyperparameters
}


fitted_models = {}

for model_name, model_pipeline in pipelines.items():
print('\nStarting to fit: {}'.format(model_name))

# Fit each model
model = RandomizedSearchCV(
    pipelines[model_name],
    hyperparameters[model_name],
    n_iter    = 15,
    n_jobs    = -1, # Use all my computer's power
    cv        = 5,  # The default value of this will change to 5 in version 0.22
    verbose   = 20,
    scoring   = 'precision'
)

model.fit(x_train, y_train)
fitted_models[model_name] = model

# Calculate predictions
y_pred = model.predict(x_test)  # y_pred = predicted Y value
y_pred_proba_prep = model.predict_proba(x_test) 
y_pred_proba = [p[1] for p in y_pred_proba_prep] # y_pred_proba = the probability of the prediction

# Calculate all the performance scores:
roc_score = roc_auc_score(y_test, y_pred_proba)
precision = precision_score(y_test,y_pred)
recall = recall_score(y_test,y_pred)
f1 = f1_score(y_test,y_pred)

print('''\n\nModel {} has been fitted. \n
      ROC AUC: {}\n
      Precision: {}\n
      Recall: {}\n
      F1: {}\n
      '''.format(model_name, roc_score, precision, recall, f1)
     )