<｜begin▁of▁sentence｜>### Instruction:
Imagine we're working on a documentary about the intricate social interplay within a small community, taking inspiration from the classic tale of Zachary's Karate Club. As we aim to visually represent the complex network of relationships on screen, we're contemplating a more digestible, simplified form for the audience. Think of it as translating a multidimensional story to a flat screen. To achieve this, we require a technique that can condense the elaborate social network into a version with fewer dimensions without losing the essence of the story.

We've come across this cutting-edge editing tool in the graspologic suite that is akin to a discerning editor, capable of determining the perfect balance of complexity and comprehensibility for our narrative. It's called the profile likelihood method, which, in film terms, helps us figure out how many 'plot lines' we need to preserve to keep the story true to its core.

As the lead producer, I want to entrust this task to you. You need to use this tool from graspologic to finesse our footage – in our case, to pinpoint the optimal number of dimensions required to succinctly portray the social ties of the Karate Club on screen. Once you've found that sweet spot of storytelling, make sure to let us know. It's a crucial piece of our production puzzle, blending scientific precision with cinematic flair.
### Response:
import numpy as np
from graspologic.embed import AdjacencySpectralEmbed
from graspologic.embed import select_dimension
import networkx as nx

# Step 1: Create a networkx graph
G = nx.karate_club_graph()

# Step 2: Embed the graph using Adjacency Spectral Embedding
ase = AdjacencySpectralEmbed()
embedding = ase.fit_transform(G)

# Step 3: Use select_dimension to determine the optimal embedding dimension
elbows, sing_vals = select_dimension(embedding, n_elbows=2)

# Print the optimal embedding dimensions
print('Optimal embedding dimensions:', elbows)
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