<｜begin▁of▁sentence｜>### Instruction:
Let's imagine we're working on a career guidance platform and we've built a robust network that captures interactions between 200 professionals and their connection with approximately 400 job-related events. As a career counselor aiming to provide clear and concise advice to clients, we need a method to visually explain the complex relationships within this professional network.

To simplify this network for our clients, could we use the `AdjacencySpectralEmbed` from the graspologic toolkit? This tool should help us translate the intricate structure of our professional network into an easier-to-understand, condensed format, while retaining the essential connections and relationships. How would this process assist in distilling the network's core properties into a form that's more approachable for career planning discussions?
### Response:
import networkx as nx
from graspologic.embed import AdjacencySpectralEmbed

# Given a graph with specified number of nodes and edges
G = nx.gnm_random_graph(200, 400)

# Using AdjacencySpectralEmbed with specified parameters
ase = AdjacencySpectralEmbed(dimensions=3, svd_solver_algorithm='randomized')

# Fit the model and obtain the latent positions (embedding)
embedding = ase.fit_transform(G)

# Output the resulting embedding
print(embedding)
<|EOT|>
