<｜begin▁of▁sentence｜>### Instruction:
As a Machine Learning Engineer, you're tasked with developing algorithms to optimize various processes. Imagine you're working on a logistics project for a shipping company. You have a dataset containing information about routes between different warehouses, including the distances and weights of the goods being transported. Now, your goal is to calculate the average distance traveled by shipments, considering the weights of the goods. To achieve this, you need to utilize a graph representation of the routes, where each edge represents a route between warehouses, and the weight of each edge corresponds to the distance and the weight of the goods being transported. In this context, you're essentially looking to calculate the average path length in the graph, considering the weights associated with each edge.
### Response:
from igraph import Graph

# Assuming 'edges' is a list of tuples representing the edges of the graph
g = Graph(edges)

# Calculate the average path length; assuming the graph is directed and weighted
avg_path_length = g.average_path_length(directed=True, unconn=False, weights='weight')

print('Average path length:', avg_path_length)
<|EOT|>
