<｜begin▁of▁sentence｜>### Instruction:
Imagine we are investigating the intricate network of nutrient exchange within a plant's cellular structure. For simplicity, we've labeled each transport channel by its function, using terms such as 'apple', 'banana', and 'cherry'. Our current map of these channels is represented by a series of connections: ['apple' to 'banana', 'banana' to 'cherry']. 

In preparation for a more quantitative analysis, we need to translate this functional map into a numerical format amenable to computational models. Specifically, we must assign each channel a unique number, beginning with 0, yet we must maintain an index of the original functional terms. This transformation is akin to recoding the genetic information while keeping a key to the original gene names.

How might we accomplish this remapping in our nutrient exchange data, ensuring that the complexity of the system is retained in a numerical structure?
### Response:
import networkx as nx

# Create a graph with non-integer labels
H = nx.Graph()
H.add_edges_from([('apple', 'banana'), ('banana', 'cherry')])

# Convert node labels to integers
G = nx.convert_node_labels_to_integers(H, first_label=0, ordering='default', label_attribute='old_label')

# Print the relabeled graph
for node, data in G.nodes(data=True):
    print(f'Node {node} was originally {data.get("old_label", "Not available")}')
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