clear up the documentation

Author: shivlaks

src/stepfunctions/steps/sagemaker.py

@@ -68,7 +68,9 @@ def __init__(self, state_id, estimator, job_name, data=None, hyperparameters=Non
                 * (list[sagemaker.amazon.amazon_estimator.RecordSet]) - A list of
                     :class:`sagemaker.amazon.amazon_estimator.RecordSet` objects,
                     where each instance is a different channel of training data.
-            hyperparameters (dict, optional): Specify the hyperparameters that are set before the model begins training. If hyperparameters provided are also specified in the estimator, the provided value will used. (Default: Hyperparameters specified in the estimator will be used for training.)
+            hyperparameters (dict, optional): Parameters used for training.
+                    Hyperparameters supplied will be merged with the Hyperparameters specified in the estimator.
+                    If there are duplicate entries, the value provided through this property will be used. (Default: Hyperparameters specified in the estimator.)
             mini_batch_size (int): Specify this argument only when estimator is a built-in estimator of an Amazon algorithm. For other estimators, batch size should be specified in the estimator.
             experiment_config (dict, optional): Specify the experiment config for the training. (Default: None)
             wait_for_completion (bool, optional): Boolean value set to `True` if the Task state should wait for the training job to complete before proceeding to the next step in the workflow. Set to `False` if the Task state should submit the training job and proceed to the next step. (default: True)