<｜begin▁of▁sentence｜>### Instruction:
As an optometrist, you may encounter a situation where you need to create a visual representation of a patient's vision health data. Using specialized tools like Optomap and OCT machines, you can gather detailed information about the patient's eye health, such as refractive errors, eye diseases, or injuries. You may need to analyze this data and present it in a clear and precise manner. Suppose you want to visualize the data of a patient's eye health using a custom graph. In this case, you can use Python's NetworkX library and Matplotlib to draw a graph with a custom bounding box, allowing you to represent the data in a visually appealing way. Additionally, by employing graphviz_layout to determine the graph layout, you can provide a clear and organized visualization of the patient's eye health data within a defined area.
### Response:
from networkx.drawing.nx_agraph import graphviz_layout
import matplotlib.pyplot as plt
import networkx as nx

def draw_graph_with_custom_bounding_box(G):
    pos = graphviz_layout(G)
    plt.figure(figsize=(8, 6))
    nx.draw(G, pos, with_labels=True)
    plt.xlim(100, 700)  # Setting x-axis limits to act as a bounding box on x-dimension
    plt.ylim(100, 500)  # Setting y-axis limits to act as a bounding box on y-dimension
    plt.show()

# Example usage:
G = nx.petersen_graph()
draw_graph_with_custom_bounding_box(G)
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