<｜begin▁of▁sentence｜>### Instruction:
Imagine you're working with a diverse team, each individual bringing their own unique 'size' of contribution to the collaborative network—their personal strengths represented by a 'size' value. For instance, think of two team members with a contribution size of 2, and another pair whose contribution is slightly more, valued at 3. Now, suppose you've noticed these team members tend to collaborate in pairs, forming connections between them, much like a social graph where the partnerships are denoted as edges: one partnership between the first two individuals (nodes 0 and 1), and another partnership between the next pair (nodes 2 and 3).

As a life coach striving to cultivate a harmonious and effective team environment, you might be curious to measure how similar these individuals are in their levels of contribution when they choose to pair up. In technical terms, you're looking at calculating the numeric assortativity coefficient, which essentially quantifies the tendency of these team members to form partnerships with others who have a similar 'size' of contribution.

How would you employ NetworkX, the insightful tool for network analysis, to deduce this coefficient which could give you a clearer picture of the team's dynamics? Doing so can provide valuable insights, allowing you to better understand and enhance the collaboration patterns within your team.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add nodes with a numeric attribute 'size'
G.add_nodes_from([0, 1], size=2)
G.add_nodes_from([2, 3], size=3)

# Add edges between nodes
G.add_edges_from([(0, 1), (2, 3)])

# Compute the numeric assortativity coefficient
assortativity = nx.numeric_assortativity_coefficient(G, 'size')
print('Numeric assortativity coefficient:', assortativity)
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