<｜begin▁of▁sentence｜>### Instruction:
In the dynamic and meticulous environment of a medical research laboratory, imagine you're tasked with analyzing the interactions between various biochemical substances involved in a crucial study aimed at unravelling novel therapeutic targets. These interactions have been meticulously mapped out in a complex network, stored in a "biochemical_interactions.gml" file, where each edge signifies a unique interaction and is tagged with an attribute named 'color'. This 'color' attribute symbolizes the type of interaction - for instance, 'red' might indicate inhibitory interactions, while 'green' denotes stimulatory actions.

However, not all interactions have been categorized yet, and some edges in your network lack this crucial 'color' attribute. Your objective is to systematically retrieve the 'color' attribute for each interaction within this network. For those interactions that haven't been color-coded yet, you're to assign them a default color, ensuring a comprehensive and uninterrupted analysis flow.

This task is central to promoting an understanding of the complex molecular dialogues that underpin disease mechanisms, potentially unlocking new dimensions in disease treatment and prevention. How would you approach this challenge, ensuring that each interaction, categorized or not, is accounted for in your analysis?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add paths with a 'color' attribute
nx.add_path(G, [1, 2, 3], color='red')
G.add_edge(3, 4, color='blue')

# Get edge attributes by specifying the attribute name
color = nx.get_edge_attributes(G, 'color')

# Output the color attribute of edge (1, 2)
print('The color of edge (1, 2) is:', color[(1, 2)])

# Try to get a color attribute for an edge without this attribute, using a default value
print('The color of edge (2, 3) with default is:', color.get((2, 3), 'yellow'))
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