<｜begin▁of▁sentence｜>### Instruction:
As a Digital Marketing Manager at a burgeoning tech enterprise, you've ingeniously devised a digital content distribution strategy, visualizing it as a network to maximize outreach and engagement. This network is structured akin to a path graph consisting of 5 pivotal nodes, each representing a crucial stage in your campaign pipeline from content creation (node 0) to final consumer engagement (node 4). Now, to fine-tune your strategy, you seek to streamline communication and content flow across this digital ecosystem. 

Firstly, you're interested in determining the most efficient route to propel content from its inception stage (node 0) to the ultimate engagement stage (node 4), essentially calculating the shortest path length between these two nodes to ensure prompt consumer interactions. 

Additionally, understanding the dynamics of your campaign, you wish to optimize every step of the content journey. Thus, you aim to ascertain the shortest path lengths from the content creation stage (node 0) to each of the other stages within your network, ensuring a seamless and rapid transition of content through your strategic pipeline. 

This practical approach not only promises to enhance the efficacy of your marketing strategies but also ensures that your brand or product captures the digital domain's ephemeral attention span with precision and agility.
### Response:
import networkx as nx

# Create a path graph with 5 nodes
G = nx.path_graph(5)

# Compute the shortest path length from node 0 to node 4
length = nx.shortest_path_length(G, source=0, target=4)
print('Shortest path length from node 0 to 4:', length)

# Compute shortest path lengths from node 0 to all other nodes
distances = nx.shortest_path_length(G, source=0)
for target, distance in distances.items():
    print(f'Shortest path length from node 0 to {target}:', distance)
<|EOT|>
