<｜begin▁of▁sentence｜>### Instruction:
Imagine we're helping with a computer science class, and the students are currently learning about randomness in simulations using Python. Now, we want to show them how to generate reproducible results with random number generation, especially when using libraries like NetworkX in Python.

Here's our practical classroom scenario: We need a simple function, let's call it `random_float`, that should always give us the same random float when we use the same starting point, known as a seed. To ensure that our `random_float` function works correctly with NetworkX, we need to make sure that it gets a proper `RandomState` instance from the `np_random_state` utility that NetworkX offers.

So, for today's lesson, can you craft this `random_float` function and use some sort of special decoration method from NetworkX? This is just a coding trick to ensure that when we call our function with a seed like the number 42, it uses the same 'randomness rules' every time, giving us the same number.

Remember, even if the concept sounds a bit complicated, it's just like when we use different colored chalk to make a pattern on the board, and then with the same colors, we can recreate that pattern every time. If the students have data files, like a GML file named 'network.gml', that they want to include in their simulations, remind them to mention this file when designing their function. Let's see if they can integrate this concept into their coding exercise!
### Response:
import networkx as nx
from networkx.utils import np_random_state

@np_random_state('seed')
def random_float(seed=None):
    return seed.rand()

# Using the decorator to ensure a RandomState instance is passed
result = random_float(seed=42)
print('Random float:', result)
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