import tensorflow as tf

            indices.active_block_indices,
            x,
            dynamic_bsize=tf.constant(block_params.bsize_out, dtype=tf.int32),
            dynamic_bstride=tf.constant(block_params.bstrides, dtype=tf.int32),
            dynamic_boffset=tf.constant([0, 0], dtype=tf.int32),
            add=False,
            transpose=transpose,
            atomic=atomic)
    else:
        y = sbnet_module.sparse_scatter(
            q,
            indices.bin_counts,
            indices.active_block_indices,
            x,
            dynamic_bsize=tf.constant(block_params.bsize_out, dtype=tf.int32),
            dynamic_bstride=tf.constant(block_params.bsize_out, dtype=tf.int32),
            dynamic_boffset=tf.constant([0, 0], dtype=tf.int32),
            add=False,
            transpose=transpose,
            atomic=atomic)
    return y


def _batch_norm(name, x, is_training, data_format='NHWC'):
    """
    Applies batch normalization.

    :param name:       [string]    Name of the variable scope.
