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

        # mini_batch_size is a required parameter
        default_mini_batch_size = min(self.DEFAULT_MINI_BATCH_SIZE,
                                      max(1, int(num_records / self.train_instance_count)))
        use_mini_batch_size = mini_batch_size or default_mini_batch_size

        super(PCA, self)._prepare_for_training(records=records, mini_batch_size=use_mini_batch_size, job_name=job_name)