<｜begin▁of▁sentence｜>### Instruction:
Pretend we're working on a high-concept fashion photo shoot that's going to mimic the interconnectedness of trends within an industry. Each model in the shoot will represent a trend, and the number of accessories they're adorned with corresponds to how influential that trend is. I've been told that for a segment of the project, we've got four trends to represent: the first trend is quite niche, only influencing one other; two trends are moderately influential, affecting two others each; and the last is a major trendsetter, impacting three others.

Now, imagine that this influence network has to be depicted graphically in a way that could be later designed into a visual storyboard or set design. We're going to use a software called NetworkX to sketch out this web of influence. The objective here is to construct a random yet specific blueprint, analogous to our imaginative influence network, by ensuring every trend (represented by nodes in the graph) aligns perfectly with its assigned number of connections (edges).

To make sure that the chaos of creativity is consistent every time we lay out these connections, we need to apply a degree of predictability. This is similar to ensuring that each time we redo a shoot, the poses and lighting capture the same essence. Could you help me with formulating a plan where we use NetworkX to create this influence network with its edges matching the influence levels — 1, 2, 2, 3 — and crucially, make sure the randomness of the connections is kept identical across different shoots, using a predetermined random seed? We want to maintain the authenticity of our representation no matter how many times we model it.
### Response:
import networkx as nx

# Given degree sequence
sequence = [1, 2, 2, 3]

# Seed for reproducibility
seed = 42

# Create a random graph with the given degree sequence
G = nx.random_degree_sequence_graph(sequence, seed=seed)

# Print the sorted degree for each node
print(sorted((d for n, d in G.degree())))
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