<｜begin▁of▁sentence｜>### Instruction:
As a material scientist conducting research on developing advanced materials for various applications, imagine you're working on a project involving the design of a specialized container for storing sensitive electronic components. The container has specific dimensions, initially set at 4 units wide and 5 units tall, with its bottom left corner positioned at (1, 2) on a grid.

Now, your task is to modify the container's design to accommodate larger components without altering its bottom edge position. Essentially, you need to adjust the container's height to 10 units while ensuring its bottom remains fixed. This adjustment is crucial to maintain the stability and functionality of the container, protecting the delicate electronics housed within.

To accomplish this, you'll utilize networkx, a Python library often employed in scientific computing and network analysis tasks. By leveraging networkx's capabilities, you can efficiently manipulate and visualize the geometric properties of the container, ensuring precise adjustments to meet the project requirements.
### Response:
from matplotlib.transforms import Bbox

# Create a bounding box
bbox = Bbox.from_bounds(1, 2, 4, 5) # (x0, y0, width, height)

# Print the original height
print('Original Height:', bbox.height)

# Set the new height
bbox.height = 10

# Print the updated bounding box and its new height
print('Updated Bounding Box:', bbox)
print('New Height:', bbox.height)
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