<｜begin▁of▁sentence｜>### Instruction:
As a Product Demonstrator showcasing a new line of network infrastructure products at a tech expo, you have a directed graph representing the data flow between different network components. Your graph consists of edges connecting key network devices with varying data capacities to illustrate the transfer limits between them.

Your task is to utilize the `maxflow_value` function in igraph to determine the maximum flow value possible from the starting network node, labeled as vertex 0, to the destination node, labeled as vertex 3. By incorporating the edge capacities of 10, 20, 30, and 40 associated with the respective connections in your demonstration, you can effectively showcase the optimal data flow capabilities within the network architecture.
### Response:
from igraph import Graph

# Create a directed graph with the given edges and capacities
edges = [(0, 1), (1, 2), (2, 3), (0, 3)]
capacities = [10, 20, 30, 40]
g = Graph(edges=edges, directed=True)

# Set the edge attributes for capacities
g.es['capacity'] = capacities

# Calculate the maximum flow value from vertex 0 to vertex 3
max_flow_value = g.maxflow_value(source=0, target=3, capacity='capacity')

# Print the maximum flow value
print(max_flow_value)
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