<｜begin▁of▁sentence｜>### Instruction:
As an executive assistant, I often assist in coordinating information and tasks for executives to streamline their decision-making process. In this scenario, imagine you're overseeing the logistics for a warehouse operation. You're tasked with optimizing storage space by determining the size of a new container needed to accommodate two shipments. 

The first shipment, with dimensions 1x1 units, is due to arrive at coordinates (0,0) in the warehouse. The second shipment, measuring 1x1 units, is scheduled to arrive at coordinates (2,2). To efficiently plan storage, you're asked to calculate the dimensions of a container that can hold both shipments. This entails finding a new bounding box that encompasses the extents of both incoming shipments, ensuring no space is wasted and all items are properly accommodated. 

To accomplish this, you may utilize networkx, a Python library often employed for graph analysis and manipulation. By representing the coordinates of the shipments as nodes in a graph and establishing connections between them, you can determine the minimum bounding box required to contain both shipments, optimizing warehouse space usage and facilitating smooth operations.
### Response:
from igraph import BoundingBox

# Define two bounding boxes
bbox1 = BoundingBox(0, 0, 1, 1)
bbox2 = BoundingBox(2, 2, 3, 3)

# Compute the union of the two bounding boxes
union_bbox = bbox1 | bbox2

print(union_bbox)
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