<｜begin▁of▁sentence｜>### Instruction:
In the day-to-day nurturing of young lives at a pediatric care facility, where growth charts and health records intricately weave together to form a network of developmental milestones and well-being indicators – much like a graph – there appears the need to amalgamate and harmonize this information for a comprehensive overview. This is akin to having-graph1.gml, where each young individual is a node, such as node (0, 'A'), representing a child with a specific set of health benchmarks, connected to another, say node (1, 'B'), representing another aspect of their well-being by edges – in this metaphor, 'edge1' being the developmental trajectory between the two.

On a parallel note, another set of records, or-graph2.gml, holds a related but distinct set of data points with node (0, 'A') once more symbolizing a child and node (2, 'C'), possibly illustrating a different health parameter, connected by 'edge2', which may symbolize a different aspect of their growth pathway. 

The conundrum we face, as custodians of this precious data, is the discernment and extraction of the universal elements – the shared nodes and edges, those fundamental health factors and connections that are universally present in every child's journey toward maturity. How does one distill this essence, the intersection graph, that concisely encapsulates only those nodes and edges that are the common denominators across all such pediatric graphs?
### Response:
import networkx as nx

# Create two graphs
G1 = nx.Graph()
G1.add_nodes_from([(0, {'label': 'A'}), (1, {'label': 'B'})])
G1.add_edges_from([(0, 1, {'label': 'edge1'})])

G2 = nx.Graph()
G2.add_nodes_from([(0, {'label': 'A'}), (2, {'label': 'C'})])
G2.add_edges_from([(0, 2, {'label': 'edge2'})])

# Calculate the intersection of G1 and G2
R = nx.intersection_all([G1, G2])

# Print nodes and edges of the resulting graph
print('Nodes:', R.nodes(data=True))
print('Edges:', R.edges(data=True))
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