<｜begin▁of▁sentence｜>### Instruction:
As an optometrist, you're accustomed to diagnosing and managing various vision and eye health conditions to ensure optimal visual acuity and comfort for your patients. Just as you use specialized tools and techniques to assess eye health, imagine you have a tool called GraphVision, which assists in visualizing and analyzing graphs representing complex networks.

Recently, a colleague handed you a graph representing a network of connections between different data points. The graph contains nodes labeled 0, 1, 2, and 3, with edges connecting them in the following pairs: (0, 1), (0, 2), (1, 2), and (1, 3). To better understand the structure of this network, you decide to utilize the GraphVision software.

Now, you're curious about how these nodes are positioned relative to each other in the graph. To gain insight into their spatial arrangement, you plan to use the GraphVision API's 'circular' layout algorithm, which arranges the nodes in a circular pattern. By visualizing the circular layout, you hope to glean valuable insights into the relationships and connections within the network, much like examining the optic nerve's distribution in an eye exam.

With GraphVision's advanced visualization capabilities, you're equipped to explore and analyze the intricate networks underlying various datasets, enhancing your ability to make informed decisions and recommendations in your field.
### Response:
from igraph import Graph

# Define the edges of the graph
g = Graph(edges=[(0, 1), (0, 2), (1, 2), (1, 3)])

# Get the circular layout of the graph
circular_layout = g.layout('circular')

# Print the layout coordinates
print(circular_layout.coords)
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