<｜begin▁of▁sentence｜>### Instruction:
Imagine we're constructing a network map to better understand the collaborative connections between different therapists in a therapy center. We have two distinct categories of nodes representing individual therapists [1, 2] and therapy techniques ['A', 'B']. Consider that these therapists share techniques, and we've been keeping track of their collaboration intensity through a points system with therapist 1 using technique 'A' extensively (3 points), technique 'B' moderately (2 points), while therapist 2 has employed technique 'A' but to a lesser extent (1 point).

Our goal is to create a simplified map that shows direct collaborations between the therapists, whereby the strength of their connection is determined by the combined "collaboration points" arising from shared techniques. How could we synthesize this information into a weighted collaboration graph that focuses solely on the therapists, effectively revealing the teamwork dynamics within our therapy center based on the shared techniques?
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Define a custom weight function
def my_weight(G, u, v):
    w = 0
    for nbr in set(G[u]) & set(G[v]):
        w += G[u][nbr].get('weight', 1) + G[v][nbr].get('weight', 1)
    return w

# Create a bipartite graph B
B = nx.Graph()
# Add nodes with the bipartite attribute
B.add_nodes_from([1, 2], bipartite=0)
B.add_nodes_from(['A', 'B'], bipartite=1)
# Add edges between nodes of opposite sets
B.add_edges_from([(1, 'A'), (1, 'B'), (2, 'A')])
# Add weights to the edges
B[1]['A']['weight'] = 3
B[1]['B']['weight'] = 2
B[2]['A']['weight'] = 1

# Project graph onto set of nodes [1, 2] using the custom weight function
G = bipartite.generic_weighted_projected_graph(B, [1, 2], weight_function=my_weight)

# Output the weighted edges
print(list(G.edges(data=True)))
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