Set hyperparameters needed for training.

        Args:
            * records (:class:`~RecordSet`): The records to train this ``Estimator`` on.
            * mini_batch_size (int or None): The size of each mini-batch to use when training. If ``None``, a
                default value will be used.
            * job_name (str): Name of the training job to be created. If not specified, one is generated,
                using the base name given to the constructor if applicable.

def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):
        """Set hyperparameters needed for training.

        Args:
            * records (:class:`~RecordSet`): The records to train this ``Estimator`` on.
            * mini_batch_size (int or None): The size of each mini-batch to use when training. If ``None``, a
                default value will be used.
            * job_name (str): Name of the training job to be created. If not specified, one is generated,
                using the base name given to the constructor if applicable.
        """
        super(AmazonAlgorithmEstimatorBase, self)._prepare_for_training(job_name=job_name)

        feature_dim = None

        if isinstance(records, list):
            for record in records:
                if record.channel == 'train':
                    feature_dim = record.feature_dim
                    break
            if feature_dim is None:
                raise ValueError('Must provide train channel.')
        else:
            feature_dim = records.feature_dim

        self.feature_dim = feature_dim
        self.mini_batch_size = mini_batch_size