def METHOD_NAME() -> None:  # pragma: no cover
    """Route GPU memory allocation via TensorFlow.
    This is recommended for using TensorFlow and cupy together, as otherwise
    OOM errors can occur when there's available memory sitting in the other
    library's pool.
    We'd like to support routing PyTorch memory allocation via Tensorflow as
    well (or vice versa), but do not currently have an implementation for it.
    """
    assert_tensorflow_installed()
    pools = context_pools.get()
    if "tensorflow" not in pools:
        pools["tensorflow"] = cupy.cuda.MemoryPool(allocator=cupy_tensorflow_allocator)
    cupy.cuda.set_allocator(pools["tensorflow"].malloc)