<｜begin▁of▁sentence｜>### Instruction:
Imagine you are the mastermind behind the visual representation of a new logistics network for a product line. The network comprises various checkpoints (nodes), with pathways (edges) connecting them. Each pathway has an associated transportation cost that needs to be visually represented for the ease of packaging workflow planners. The checkpoint and pathway data are as follows: 

Checkpoints and Pathways:
- Connection from Checkpoint 1 to Checkpoint 2 has a cost of 5
- Connection from Checkpoint 1 to Checkpoint 3 has a cost of 3
- Connection from Checkpoint 2 to Checkpoint 3 has a cost of 1
- Connection from Checkpoint 3 to Checkpoint 4 has a cost of 7

And the planners have preferred that checkpoints be arranged in the sequence: [1, 2, 3, 4].

In the sphere of your creative expertise, how would you transpose this logistical framework into a numerical adjacency matrix, ensuring that every matrix element reflects the cost that connects the corresponding checkpoints? Your designed matrix will be a crucial tool to guide the packaging team in determining the most cost-efficient routing for product distribution.
### Response:
import networkx as nx
import numpy as np

# Create a graph
G = nx.Graph()
G.add_edge(1, 2, weight=5)
G.add_edge(1, 3, weight=3)
G.add_edge(2, 3, weight=1)
G.add_edge(3, 4, weight=7)

# Use the `to_numpy_array` function to create the adjacency matrix
nodelist = [1, 2, 3, 4]
A = nx.to_numpy_array(G, nodelist=nodelist, dtype=np.float64, weight='weight')

print(A)
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