<｜begin▁of▁sentence｜>### Instruction:
Imagine you are working at a healthcare facility, and you’ve been given the task of streamlining the patient transfer process between two departments, G1 and G2. Department G1 currently handles a group of 4 patients, while department G2 is designed to manage 5 patients effectively. 

Your job is to efficiently restructure G1 so it can operate in a manner that mirrors the patient flow in G2. To achieve this, consider the following interventions (akin to medical procedures): You might need to ‘substitute’ certain patient management protocols from G1 with those from G2, ‘remove’ some of the less efficient steps in G1’s process, or ‘introduce’ new steps to fill in any gaps.

How would you calculate the least number of adjustments needed for G1's patient management model to become functionally identical to that of G2? This measurement is crucial as it helps maintain a high standard of patient care while ensuring uniformity in service delivery across the departments.
### Response:
import networkx as nx
from networkx.algorithms.similarity import optimize_edit_paths

# Define two graphs G1 and G2 of the same type
G1 = nx.path_graph(4)
G2 = nx.path_graph(5)

# Define a node substitution cost function
node_subst_cost = lambda n1, n2: 1 if n1 != n2 else 0

# Define a node deletion cost function
node_del_cost = lambda n: 1

# Define a node insertion cost function
node_ins_cost = lambda n: 1

# Calculate edit paths using custom costs
edit_paths = list(optimize_edit_paths(G1, G2,
                                      node_subst_cost=node_subst_cost,
                                      node_del_cost=node_del_cost,
                                      node_ins_cost=node_ins_cost))

# Output the edit paths with the corresponding costs
for path in edit_paths:
    print(path)
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