<｜begin▁of▁sentence｜>### Instruction:
Imagine you're working on a complex digital photo project where you need to visualize the connections between various elements of your composition. To help you map out the relationships and the flow of the project, you're using a tool similar to the way a photo retoucher would use layers and masks to manage different parts of an image. Your tool is NetworkX, a software library for creating, manipulating, and studying complex networks.

You have a special photo composition represented as a "Lollipop graph" which has been crafted with `nx.lollipop_graph(4, 3)` in NetworkX, akin to a unique blend of two distinct sections in an image. Within this graphical model, the connection—or "edge"—between elements 1 and 2 has been assigned an attribute, similar to the way you'd set an opacity level on a layer, and this particular attribute is known as 'weight' with a value of 3. Similarly, another connection in your composition between elements 3 and 4 is designated with a 'capacity' attribute set at 12, much like setting a blending mode for a layer to define how it interacts with elements beneath.

Now, to properly organize and document the relationships in your composition, you wish to export an "edge list"—a breakdown of all the connections and their attributes—using NetworkX's `generate_edgelist` function. Yet, you need this list in various formats for different purposes: one is a simple outline with no specific edge data, just a clean chart of connections. Another is a more detailed breakdown that includes the full set of attributes for each edge as a dictionary, which might be akin to a detailed layer panel display in Photoshop. And finally, you need a tailored list that highlights just the 'weight' attributes, akin to isolating just the opacity values of your layers for closer inspection.

Now, let's see how you would produce these different formats of your edge list without actually seeing the lines of code or the output. Think of it as preparing your files for different stages of the retouching process; each format providing a different level of insight into the structure of your digital creation.
### Response:
import networkx as nx

# Create a lollipop graph
G = nx.lollipop_graph(4, 3)

# Add some edge attributes
G[1][2]['weight'] = 3
G[3][4]['capacity'] = 12

# Example with no data
for line in nx.generate_edgelist(G, data=False):
    print(line)

# Example with default data representation
for line in nx.generate_edgelist(G):
    print(line)

# Example with specific data keys
for line in nx.generate_edgelist(G, data=['weight']):
    print(line)
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