<｜begin▁of▁sentence｜>### Instruction:
In the realm of network sciences, a fascinating topic recently came to light as researchers consider the dynamics of directed graphs—structures made up of nodes and edges, with each edge possessing a certain weight indicative of its significance or capacity. Imagine a situation where we have mapped out a particular network consisting of several paths: from node 1 to node 2 with a weight of 0.5, from node 1 to node 3 with a weight of 1.5, another from node 2 to node 3 bearing a weight of 2.0, and finally an intriguing reverse flow from node 3 back to node 1 weighing in at 0.7.

Now, specialists in the field are pondering over how this network can be transformed into what's known as a right-stochastic graph—a term that might sound daunting but simply refers to a network where the total weight of paths leading out from a single node adds up to the proverbial 'whole pie' or, numerically speaking, 1. This transformation process isn't merely an act of division and normalization; it must take into account the original relational weights that have been meticulously recorded.

The scientific community is abuzz, looking for ways to recalibrate this graph while preserving the essence of its connections. It raises the question of how to ensure that the complex interplay between nodes remains accurate and reflective of the initial situation, despite the modification to meet the right-stochastic criterion.

It's a puzzle that stands at the intersection of abstract mathematical concepts and their practical applications, and the solution has implications for various fields—ranging from sociology, where such graphs could represent influence between individuals, to biology, where they could model the flow of resources in an ecosystem. How to strike a balance between retaining the original weight proportions and achieving a harmonious sum equivalent to unity is a challenge that remains to be addressed.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_weighted_edges_from([(1, 2, 0.5), (1, 3, 1.5), (2, 3, 2.0), (3, 1, 0.7)])

# Convert to right-stochastic graph
stochastic_G = nx.stochastic_graph(G, copy=True)

# Print the edge weights
for edge in stochastic_G.edges(data=True):
    print(edge)
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