<｜begin▁of▁sentence｜>### Instruction:
Imagine we are sitting in a collaborative meeting with a group of speech-language pathologists discussing the intricacies of communication networks and how they might metaphorically represent the complex web of interactions we see in language development and speech therapy. It strikes us that these networks could be a brilliant educational tool, so we decide to create a visual representation using software.

We have a graph encoded in a brief sequence of bytes—'A_'—analogous to the concise yet potent ways we encapsulate complex language patterns when we assess speech. This graph speaks a language of its own, one we wish to translate into a visual form to better understand the interconnectivity at play. To do this using the NetworkX library, we would turn the encoded graph into a functional NetworkX graph object.

The question thus becomes: How shall we use the function from_graph6_bytes to breathe life into this abstract representation, transmuting those bytes into a tangible NetworkX graph? Moreover, once materialized, which connections—akin to the synaptic links within a developing brain—will be revealed as the edges comprising this graph's anatomy?
### Response:
import networkx as nx

# Given graph6 bytes for a simple graph
bytes_input = b'A_'

# Decode the graph6 bytes to a NetworkX graph
G = nx.from_graph6_bytes(bytes_input)

# Print the edges of the graph
print(sorted(G.edges()))
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