<｜begin▁of▁sentence｜>### Instruction:
Imagine we're orchestrating a major conference where network scientists will convene to share insights on graph algorithms. An integral workshop will focus on the reproducibility of experiments involving stochastic processes in graph analysis. For this workshop, we aim to prepare a demonstration illustrating the use of NetworkX to handle such algorithms with consistent outcomes across sessions.

In this context, I'm tasked with prepping the materials and I need to ensure that the random elements in the demonstrations can be replicated with precision. To that end, could you advise how the 'create_py_random_state' function from NetworkX might be utilized? Specifically, we need to configure it so that it produces a fixed random state anchored by the seed value of 42, ensuring that the stochastic graph algorithms wielded during the workshop lead to uniform results when they’re executed.
### Response:
import networkx as nx

# Create a random state
random_state = nx.utils.create_py_random_state(42)

# Use the random state
count = random_state.randint(1, 100)
print(f'Random number between 1 and 100: {count}')
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