<｜begin▁of▁sentence｜>### Instruction:
Alright! Let's imagine, you're preparing for an amazing journey of bringing new life into the world, and just as you guide expectant parents through the wonders of childbirth, explaining the complex process in a nurturing and comprehensible way, let me take you through the delicate procedure of extracting a smaller network from a larger complex web of relationships in our data.

Now, just as in childbirth where we focus on the essentials of what to expect during labor, I'll focus on the critical elements of our network graph. Think of the original graph as an expecting mother. It's complete with a vast network of connections, much like the web of support, information, and care around the journey of pregnancy. 

Within this large network, we often want to zoom in and understand smaller, more manageable parts. This is somewhat akin to how we would focus on breathing techniques during labor, isolating them out of the wide array of pregnancy-related experiences. Similarly, in our network analysis, we might want to isolate a subset - a subgraph, composed of a specified number of connections (or "edges", in graph terminology).

The "RandomEdgeSamplerWithInduction" is our tool for this task. Think of it as selecting a manageable number of childbirth techniques to teach in one session, and then further ensuring that the parents understand them deeply through practice - that's what "induction" is in our context, making sure the selected edges are connected to form a sensible pattern that we can analyse.

In our real-life scenario, what we want is to load our complex web from a 'littleballoffur6.sparse6' format - let's liken that to referencing a well-researched childbirth manual. From this, we're aiming to carefully handpick a certain number of connections and ensure they form a comprehensible subgraph, something that can be a good representation of the larger whole, yet simpler to understand and analyse.

Taking this into the realm of our network graph analysis, could you guide me through using the RandomEdgeSamplerWithInduction to sample a subgraph that contains a predetermined number of edges from a graph originally read from the 'littleballoffur6.sparse6' file, and then use edge set induction to ensure all sampled edges are part of a connected component? If the data were in a .gml file, I would ask for the equivalent gml file name.

Hope that paints the picture! Now, shall we delve into the specifics of our data sampling procedure?
### Response:
import networkx as nx
from littleballoffur import RandomEdgeSamplerWithInduction
G = nx.read_sparse6('littleballoffur6.sparse6')
sampler = RandomEdgeSamplerWithInduction(number_of_edges=50, seed=42)
sampler_G = sampler.sample(G)
print(sampler_G.edges())
print(sampler_G.nodes());
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