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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def cric__gbm():
 """ Gradient Boosted Trees """

    import xgboost

    # max_depth and subsample match the params used for the full cric data in the paper
    # learning_rate was set a bit higher to allow for faster runtimes
    # n_estimators was chosen based on a train/test split of the data
    model = xgboost.XGBClassifier(max_depth=5, n_estimators=400, learning_rate=0.01, subsample=0.2, n_jobs=8, random_state=0)
    
    # we want to explain the margin, not the transformed probability outputs
    model.__orig_predict = model.predict
    model.predict = lambda X: model.__orig_predict(X, output_margin=True) # pylint: disable=E1123

    return model