<｜begin▁of▁sentence｜>### Instruction:
As a legal representative, I am often asked to navigate complex situations and find the most effective path forward. Similarly, you are seeking guidance on finding the shortest path from one point to another using the A-Star algorithm with a specific heuristic function. In this case, we can apply this algorithm to a network with nodes representing different legal entities and edges representing the relationships or legal dependencies between them. The weights on the edges could denote the strength or significance of these legal connections, guiding the algorithm to identify the most efficient route between two legal entities. Through this approach, we can draw parallels between legal problem-solving and network analysis to find optimal solutions in both domains.
### Response:
import igraph as ig

# Create a graph with weighted edges
g = ig.Graph(edges=[(0,1), (1,2), (0,2), (2,3)], directed=False)
g.es['weight'] = [1, 3, 2, 4]

# Define a heuristic function considering the 'weight' attribute
def heuristic(graph, vertex1, vertex2):
    return abs(graph.vs[vertex1]['weight'] - graph.vs[vertex2]['weight'])

# Calculate the shortest path using A* algorithm
path = g.get_shortest_paths_astar(0, 3, weights='weight', heuristic_func=heuristic)

# Print the shortest path
print('Shortest path (A*):', path)
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