<｜begin▁of▁sentence｜>### Instruction:
As a media buyer, you've been tasked with creating a visually compelling presentation for a new marketing campaign. You've gathered network data representing the flow of advertising outreach from initial engagement to final conversion, with nodes labeled 0 through 4. To effectively differentiate the edges in this directed network, you want to utilize the GradientPalette library's GradientPalette.get_many function to apply a color gradient that visually distinguishes the progression of the advertising flow. How can you achieve this using the GradientPalette library's capabilities?
### Response:
import networkx as nx

# Create a directed graph
g = nx.DiGraph()

# Add nodes to the graph
for i in range(5):
    g.add_node(i)

# Add edges to the graph
edges = [(0, 1), (1, 2), (2, 3), (3, 4)]
g.add_edges_from(edges)

# Use a color gradient for visualization
number_of_colors = g.number_of_edges()
gradient_palette = GradientPalette(['red', 'green', 'blue'])
colors = gradient_palette.get_many(number_of_colors)

# Visualize the graph using the colors
nx.draw(g, node_color='black', edge_color=colors, with_labels=True)
plt.show()
<|EOT|>
