<｜begin▁of▁sentence｜>### Instruction:
Imagine you are a registered dietitian and you have mapped out a nutritional improvement plan for a group of patients. The plan is structured as a series of sequential dietary changes that are represented as a network with each step or change having a certain impact factor. It could either have a positive effect (like introducing more fruits and vegetables) or a temporary negative effect (like the hunger pangs associated with reducing sugar intake). The steps are connected and each has a numerical value signifying the impact factor.

For example, you start with a baseline dietary plan (node 0), then introduce a step that improves their diet quality but it's initially challenging (an edge from node 0 to node 1 with an impact factor of 2), following a negative impact step such as a tough but necessary dietary cutback (an edge from node 1 to node 2 with an impact factor of -5), and so on until the final dietary goal (node 4) is achieved.

You are trying to find the most effective pathway from the baseline to the final goal while considering the varying impact factors at each step. How can you calculate the most efficient improvement track from the baseline dietary plan (node 0) to all the subsequent dietary steps (nodes 1 to 4) using this network data?
### Response:
import networkx as nx

# Create a weighted graph
G = nx.DiGraph()
G.add_weighted_edges_from([(0, 1, 2), (1, 2, -5), (2, 3, 1), (3, 4, 7)])

# Compute shortest paths from source node 0
try:
  path = nx.single_source_bellman_ford_path(G, 0)
  print('Shortest path from node 0 to 4:', path[4])
except nx.NodeNotFound as e:
  print('Error:', e)
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