<｜begin▁of▁sentence｜>### Instruction:
As an epidemiologist, suppose I'm working with a complex contact tracing network graph generated using NetworkX, and I've painstakingly calculated the optimal node positions to accurately reflect spatial relationships and interactions. My goal is to produce a compelling visual representation of this graph to aid in the public health analysis. To that end, could you instruct me as to how one might utilize NetworkX to generate and export a meticulously crafted image of the network?

The visualization should include customized node colors indicative of different transmission risk levels, tailored visual styles to represent various data points clearly, and it needs to be suitable for inclusion in both light and dark-themed reports. This nuanced visual documentation is imperative to effectively communicate our findings and action plans.
### Response:
import networkx as nx
from graspologic.layouts import save_graph, NodePosition

# Create a graph
G = nx.fast_gnp_random_graph(10, 0.5)

# Let's assume we have a precomputed dictionary of positions
positions = {node: NodePosition(node, np.random.rand(), np.random.rand()) for node in G.nodes()}

# Define node colors
colors = {node: '#1f78b4' for node in G.nodes()}

# Save the graph to an image file
output_path = 'output_graph.png'
save_graph(output_path=output_path, graph=G, positions=positions, node_colors=colors,
           vertex_line_width=0.01, vertex_alpha=0.55, edge_line_width=0.5, edge_alpha=0.02,
           figure_width=15.0, figure_height=15.0, light_background=True,
           vertex_shape='o', arrows=False, dpi=100)
<|EOT|>
