<｜begin▁of▁sentence｜>### Instruction:
Imagine we're overseeing a brand's marketing strategy that prides itself on data-driven decisions, and we're leveraging a social network analysis to understand consumer relationships and influence patterns. As part of this, we're using a custom-made Python script that employs NetworkX's np_random_state decorator to ensure consistency in our simulations. 

We have two essential functions within our script: 'random_float', which is designed to generate a consistent random float based on a given seed, ensuring that our market simulations yield the same results each time for comparison purposes; and 'random_array', which creates a dimensional array of random numbers, again reproducible by using the same initial seed and random state.

Could you explain, using terminology that suits our branding focus, how the np_random_state decorator within NetworkX guarantees that these functions will produce the same sequences of random numbers and thus provide reliable data for our branding strategy, given the same seed is used in each campaign analysis? It's critical for our approach that each time we run our marketing simulations, the results are reliably consistent, as this could have significant implications for how we position our brand in the market.
### Response:
import networkx as nx
import numpy as np

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

@nx.utils.decorators.np_random_state(0)
def random_array(dims, rng=None):
    return rng.rand(*dims)

# Using the functions
decorated_random_float = random_float(seed=42)
decorated_random_array = random_array((2, 3), rng=np.random.RandomState(42))

print('Single random float:', decorated_random_float)
print('Random array:\n', decorated_random_array)
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