How to pass lambda hypterparameter to Sagemaker XGboost estimator with set_hyperparameters

xgb = sagemaker.estimator.Estimator(**training_dict, sagemaker_session=sagemaker_session)

xgb.set_hyperparameters(    num_round = 2000,
                            objective = 'binary:logistic', 
                            tree_method = 'hist', 
                            eval_metric = 'auc', 
                           .
                           .
                           .
                            lambda = 0.5,
                            alpha = 1
                       )
xgb.fit({'train': s3_input_train, 'validation': s3_input_validation}) 

The documentation here lists lambda for L2 Regularization but when I pass this to the set_parameter method for sagemaker estimator I get a syntax error because lambda is keyword.

https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost_hyperparameters.html

    lambda = 0.5, 
           ^
SyntaxError: invalid syntax

lambda is a reserved keyword in Python for lambda expressions.

The way you can get around it is to rather put your function arguments in a dict and then "unpack" the dict into function arguments:

xgb.set_hyperparameters(**{ "num_round": 2000,
                           .
                           .
                           .
                            "lambda": 0.5,
                            "alpha": 1
                       })