how can I shuffle node labels and get a new weight vector using NumPy in Python?

I am saving the edge weights of an undirected graph in a row vector. For instance, if I have a graph as pictured below

The vector that I create is [5, 3, 4, 1, 2, 7] as ordered based on node number in ascending order. Now, if I swap the node labels of nodes 1 and 4, I can obtain the following graph;

In this scenerio, the vector that I should have is [2, 7, 4, 1, 5, 3]. My question is if I have an n by m NumPy array, where n is the number of graphs and m is the number of edges, how can I shuffle the node labels for each row and get the updated array efficiently?

Suppose I have a set of graphs consisting of four nodes as shown below. My intention is to randomly shuffle node labels in each network and then get an updated weights accordingly in a same size array.

np.random.seed(2)
arr = np.random.randint(10, size=(5, 6))
arr
array([[8, 8, 6, 2, 8, 7],
       [2, 1, 5, 4, 4, 5],
       [7, 3, 6, 4, 3, 7],
       [6, 1, 3, 5, 8, 4],
       [6, 3, 9, 2, 0, 4]])