<｜begin▁of▁sentence｜>### Instruction:
Imagine we're in the fast-paced environment of an intensive care unit (ICU), where every piece of equipment and monitoring tool is vital to the care of our patients. In this scenario, consider the network of healthcare professionals and the intricate pattern of interactions as a complex web, much like a patient's circulatory system.

Now, envision that we want to analyze this network's structure to improve our understanding of collaborative care patterns. Each member of our medical team has a specific number of one-on-one collaborations (akin to the independent edge degrees) and group involvements in patient care (much like the triangle degrees). We're interested in constructing a stylized, randomized model of these professional interactions using NetworkX, a powerful tool for analyzing such networks.

After we build this representative network model, we notice there might be redundant connections and some connections that are essentially 'short-circuits'—pointless loops of information that don't contribute to patient care. Clearly, we need to streamline this model to reflect only meaningful connections. Essentially, we aim to remove any parallel collaborations and any self-focused loops where a team member might be recorded as 'collaborating' with themselves.

Can you assist us in setting up this model, ensuring that it represents our ICU team's joint degree sequence of independent collaborations and group interactions while omitting redundant and self-looped connections?
### Response:
import networkx as nx

# Define the joint degree sequence
joint_degree_sequence = [(1, 0), (1, 0), (1, 0), (2, 0), (1, 0), (2, 1), (0, 1), (0, 1)]

# Generate the random clustered graph
G = nx.random_clustered_graph(joint_degree_sequence)

# Convert to a standard graph, removing parallel edges
G = nx.Graph(G)

# Remove self-loops
G.remove_edges_from(nx.selfloop_edges(G))
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