<｜begin▁of▁sentence｜>### Instruction:
Imagine you're preparing for a photography exposition with the theme "Connecting Worlds," and you've decided to represent the diverse interactions within a community through a visual metaphor of a bipartite graph. You have two sets of photos: one featuring portraits labeled 'A', 'B', 'C', 'D' and another showcasing landscapes labeled 'E', 'F', 'G', 'H'. Each portrait is paired with a landscape, creating a unique combined piece, much like edges in a graph: 'A' with 'E', 'B' with 'F', 'C' with 'G', and 'D' with 'H'.

For your exhibit's catalog, you're compiling lists of these pairings in different formats to inform your audience. In one format, you simply want to list the pairs without any extra information. In another, you aim to include all the details, noting that the 'B'-'F' pairing has a special "blue" theme and the 'C'-'G' pairing symbolizes a strong bond, represented by a weight of 7. Finally, you seek a list focusing solely on the strength of the connections, thus only including the weight attribute where applicable.

As a digital metaphor for your catalog, utilize NetworkX's bipartite.generate_edgelist function to generate these edge lists in three formats: a simple one, a detailed one, and one highlighting specific details like the weight of the connection. How would you go about creating these varied lists to showcase the intricate connections in your photographic exhibition?
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Create a bipartite graph
G = nx.Graph()
G.add_nodes_from(['A', 'B', 'C', 'D'], bipartite=0)
G.add_nodes_from(['E', 'F', 'G', 'H'], bipartite=1)
G.add_edges_from([('A', 'E'), ('B', 'F'), ('C', 'G'), ('D', 'H')])
G['B']['F']['color'] = 'blue'
G['C']['G']['weight'] = 7

# Generate edge list without data
for line in bipartite.generate_edgelist(G, data=False):
    print(line)

# Generate edge list with all edge data
for line in bipartite.generate_edgelist(G):
    print(line)

# Generate edge list with specific edge data (e.g., 'weight')
for line in bipartite.generate_edgelist(G, data=['weight']):
    print(line)
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