<｜begin▁of▁sentence｜>### Instruction:
As an app developer, imagine you're tasked with creating a feature that helps users navigate through different levels of a content hierarchy represented by interconnected nodes. To visualize and program the underlying data structure for this feature, you might use a graph with the following connectivity: edges are defined as connections between nodes, akin to links between different pages or sections of your app.

Suppose the edge set for your app's content structure is defined as `[(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)]`. To manage the content flow efficiently, you want to implement a breadth-first search (BFS) algorithm starting from a specific section—let's say, section 1 of your app. You would like to organize all the accessible content into layers based on their distance from the starting point.

Could you rework your feature to utilize something akin to the bfs_layers function to traverse your app's content structure starting from section 1 and compile a list detailing all the content layers? This would help you optimize the user's navigation experience by ensuring they can access content in an orderly fashion, layer by layer. Remember, this should maintain the original relationships implied by the given edge set in your graph.
### Response:
import networkx as nx

# Create a graph
H = nx.Graph()
H.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)])

# Use the bfs_layers function from NetworkX
# to get the breadth-first search layers starting from node 1
layers = dict(enumerate(nx.bfs_layers(H, [1])))

print(layers)
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