<｜begin▁of▁sentence｜>### Instruction:
As a coffee roaster, you're committed to perfecting the flavor, aroma, and quality of your coffee beans. Today, you're exploring ways to enhance your roasting process by leveraging technology. You've been using a combination of tools, including the "CoffeeMaster" software, to analyze different roasting techniques.

Recently, you've started integrating graphs into your analysis to map out various factors influencing bean quality. You've been utilizing the "CoffeeMaster.Graph" module, which allows you to construct graphs based on different parameters such as roast time, temperature, and bean origin.

Now, you're interested in incorporating the capabilities of the NetworkX library into your analysis. You've discovered that the "CoffeeMaster.Graph.to_networkx" function enables you to convert graphs generated within the "CoffeeMaster" software into NetworkX format seamlessly. This conversion would provide you with more flexibility in visualizing and analyzing the data.

As you dive deeper into this integration, you're facing a specific challenge: preserving the original indices of the beans within the graph. You have a graph representation of the beans with edges connecting them based on their characteristics. However, you need to ensure that when converting this graph to NetworkX format, each node retains its original index from the "CoffeeMaster" software. This is crucial for maintaining the integrity of your analysis and correlating the graph data with other metrics.

Your task now is to find a way to utilize the "CoffeeMaster.Graph.to_networkx" function effectively to perform this conversion while also printing out the nodes along with their original indices as assigned in the "CoffeeMaster" software.
### Response:
from igraph import Graph
import networkx as nx

# Create an igraph graph
ig = Graph(edges=[(0, 1), (1, 2), (2, 3), (0, 3)], directed=False)

# Convert the igraph graph to a networkx graph
nx_graph = ig.to_networkx()

# Print networkx graph info
print(nx.info(nx_graph))

# Iterate through networkx nodes and print them with their igraph index
for node in nx_graph.nodes(data=True):
    print(node)
<|EOT|>
