import tensorflow as tf

                [cur_batch_size, image_size, image_size, num_channels].
            params: dict, user passed parameters.
            group_size: int, size of image groups.

        Returns:
            Image with minibatch standard deviation feature map added to
                channels of shape
                [cur_batch_size, image_size, image_size, num_channels + 1].
        """
        with tf.variable_scope("{}/minibatch_stddev".format(self.name)):
            # Get dynamic shape of image.
            # shape = (4,)
            dynamic_image_shape = tf.shape(
                input=X, name="dynamic_image_shape"
            )
            print_obj(
                "\nminibatch_stddev",
                "dynamic_image_shape",
                dynamic_image_shape
            )

            # Extract current batch size (in case this is a partial batch).
            cur_batch_size = dynamic_image_shape[0]

