<｜begin▁of▁sentence｜>### Instruction:
Imagine we're devising a plan for a new in-home care visitation schedule within a local neighborhood laid out like a simple 3x3 grid. Each intersection in the neighborhood represents a client's home, and each road connects the homes. The cost of traveling on these roads isn't the same everywhere – it's specifically twice the house number on the second corner of the street you’re turning onto. We want to determine the most efficient route for our care providers to take from the home located at the bottom-left corner of the neighborhood (house number 0, 0) to the home at the top-right corner (house number 2, 2).

To assist with this, we plan to use a digital mapping tool that incorporates the A* algorithm, known for finding the shortest path between two points efficiently. This algorithm will utilize the straight-line distance, also called the Euclidean distance, between the homes as a guide to make its calculations more efficient.

Could you draft a plan using the 'astar_path' function in the NetworkX mapping tool to determine the best route for our care providers to take? While you work on this, please ensure that the scenario remains focused on creating the most efficient visitation route that incorporates the road costs as described.
### Response:
import networkx as nx

# Create a graph
G = nx.grid_graph(dim=[3, 3]) # nodes are two-tuples (x, y)

# Set custom edge attributes for 'cost'
nx.set_edge_attributes(G, {e: e[1][0] * 2 for e in G.edges()}, 'cost')

# Define a heuristic function for A*
def dist(a, b):
    x1, y1 = a
    x2, y2 = b
    return ((x1 - x2)**2 + (y1 - y2)**2)**0.5

# Find shortest path using astar_path from (0, 0) to (2, 2)
path = nx.astar_path(G, (0, 0), (2, 2), heuristic=dist, weight='cost')
print('Shortest path from (0, 0) to (2, 2):', path)
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