<｜begin▁of▁sentence｜>### Instruction:
In the realm of computational physics, consider the scenario where you're simulating a network of particles or components that are interlinked in some form of spatial configuration. Imagine you want to generate a theoretical model of these interactions using a Waxman random network comprised of 50 entities. In this network, the probability of each pair of entities being connected decreases with the Euclidean distance between them.

You are tasked with setting up a simulation environment where the location of each component is dictated by the standard Euclidean metric. For the purpose of your simulation, you decide to use specific parameters of the Waxman model—namely, alpha set to 0.4 and beta set to 0.1—to characterize the connectivity probability amongst the constituent components of your network.

Furthermore, you have to ensure the repeatability of your simulations, hence you opt to initiate the random number generator with a fixed seed value. This deterministic approach will allow you and your peers to reproduce the exact spatial network configuration in subsequent analyses or peer review processes.

Once this spatial network is constructed, you will then need to document and analyze the spatial coordinates of each component within the system. Doing so will provide essential data points that are fundamental to understanding the physicality of the network and the potential implications on its dynamics or stability.

By chance, if you were documenting these configurations, how would you proceed to encapsulate this spatial model as a gml file, if such a need arose? Could you adapt the aforementioned theoretical framework into an applicable methodology while retaining the core objectives and parameters at hand?
### Response:
import networkx as nx
import random

# Set up the random seed if you want the results to be reproducible
random_seed = 42
random.seed(random_seed)

# Use the Waxman graph generator
G = nx.waxman_graph(n=50, alpha=0.4, beta=0.1, L=None,
                    domain=(0, 0, 1, 1), metric=None, seed=random_seed)

# Access the position of a node
pos = nx.get_node_attributes(G, 'pos')

# Plot the graph
import matplotlib.pyplot as plt
nx.draw(G, pos, node_size=20)
plt.show()
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