<｜begin▁of▁sentence｜>### Instruction:
Certainly! As an insurance adjuster who is tasked with assessing the extent of coverage across various claims, imagine that you are evaluating a network of claims - akin to a series of interconnected roads, where each claim is a node on a directed path. The nodes here are sequenced from 0 to 4, with each representing a different claim, and node 4 is the final claim you wish to settle.

Just as you would calculate the distances traveled in an investigation to reach a conclusive end, in this case, you are required to determine the minimum number of steps needed to process from each of the initiating claims (nodes) to the target claim at node 4. This involves finding the shortest claim resolution pathway within your 'claims graph'.

This data does not stem from a GML file, but rather, it is a direct conceptualization of the network, where the connections (edges) point in one direction – towards a resolution at claim node 4. How would you proceed to establish these shortest pathways to the target claim 4 from all accessible claims within this directed graph structure using NetworkX tools? Remember, the precision in this analysis can significantly streamline your claims evaluation process, ensuring swift and efficient settlement operations.
### Response:
import networkx as nx

# Create a directed graph
G = nx.path_graph(5, create_using=nx.DiGraph())

# Compute the shortest path lengths to target node 4 from all reachable nodes
lengths = dict(nx.single_target_shortest_path_length(G, 4))

# Output the shortest path lengths to target node
for node in range(5):
    print(f"{node}: {lengths[node]}")
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