<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a virtual reality developer creating a VR simulation that visualizes the collaborative dynamics within a small network of project team members and their shared tasks. You have two distinct sets in your simulation: Team Members U = ['A', 'B', 'C'] and Tasks V = [1, 2, 3, 4]. The interactions between Team Members and Tasks are as follows: [('A', 1), ('B', 1), ('A', 2), ('B', 3), ('C', 3), ('C', 4)].

To enhance the user's understanding of the collaborative efforts within the team, you want to produce a network diagram that not only shows which team members are connected through common tasks but also indicates the strength of their relationships based on the number of tasks they share.

You need a function, specifically the weighted_projected_graph function, to generate a graph that represents team members (U) and visualizes the weighted connections between them corresponding to their shared task involvement. Could you reformulate the data for this projection and integrate it into your VR simulation's debugging mode, so it can display each team member node along with the ties and corresponding weights representing their shared tasks?
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Create a bipartite graph B
B = nx.Graph()
# Add nodes with the node attribute 'bipartite'
B.add_nodes_from(['A', 'B', 'C'], bipartite=0)
B.add_nodes_from([1, 2, 3, 4], bipartite=1)
# Add edges only between nodes of opposite node sets
B.add_edges_from([('A', 1), ('B', 1), ('A', 2), ('B', 3), ('C', 3), ('C', 4)])

# Use the weighted_projected_graph function to project the graph onto one set of nodes
# Here, we are projecting onto the set with nodes ['A', 'B', 'C']
G = bipartite.weighted_projected_graph(B, ['A', 'B', 'C'])

# Print the nodes and edges with weights in the resulting projection
print(list(G))
print(list(G.edges(data=True)))
<|EOT|>
