<｜begin▁of▁sentence｜>### Instruction:
Imagine that we're looking at a treatment network within a radiation oncology clinic, where various types of therapy sessions ('A', 'B', 'C', 'D') represent our nodes. Each session type is designed to target tumors of different sizes, and in this context, the 'size' attribute of a session correlates to the complexity of the cancers it's intended to treat. Sessions 'A' and 'C' are tailored toward complexities categorized as level 2, and sessions 'B' and 'D' address complexities at level 3.

Our network's connections are determined by the transition of patients between therapy sessions, as some patients may start with one type and move to another as part of their treatment plan. The specific connections are as follows: a patient often transitions from 'A' to 'B', 'A' to 'C', and 'C' to 'D'.

We are trying to evaluate if there's a pattern or correlation in the complexity transitions of these therapy sessions — namely, whether session types with similar complexities are more likely to be connected or if there's no apparent preference. To do this, we hope to calculate the numeric assortativity coefficient of our treatment network based on the 'size' attribute using NetworkX.

Could you, in your capacity, assist us by incorporating this calculation into our data analysis to enhance the way we plan and manage the progression of therapy sessions?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
# Add nodes with a 'size' attribute
G.add_node('A', size=2)
G.add_node('B', size=3)
G.add_node('C', size=2)
G.add_node('D', size=3)
# Add edges between nodes
G.add_edges_from([('A', 'B'), ('A', 'C'), ('C', 'D')])

# Compute the numeric assortativity coefficient of the graph based on the 'size' attribute
assortativity_coefficient = nx.numeric_assortativity_coefficient(G, 'size')
print('Numeric assortativity coefficient based on node size:', assortativity_coefficient)
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