<｜begin▁of▁sentence｜>### Instruction:
Imagine we're tasked with constructing a diagram that symbolizes a network of industrial facilities. We aim to design this network such that the likelihood of interaction between facilities follows a pattern seen in natural systems, where fewer large hubs dominate the connections. To achieve this, we can use a model that approximates how such relationships might distribute, which in mathematical terms is known as a power-law degree distribution.

To construct our diagram, we'll need to employ a specific method that produces a tree structure reflective of a power-law degree distribution with a particular skewness, represented by an exponent value. Our goal is to generate a tree with exactly 10 nodes, symbolizing 10 industrial facilities, and we desire the connection pattern between these nodes to mirror a power-law distribution with an exponent of 3. This will ensure that our tree-like structure of facilities has a realistic hierarchy of connections, where some nodes (facilities) are more integral than others, akin to the presence of major industrial hubs in a network of smaller sites.

Since we need to translate this model into a consistent diagram that can be revisited and analyzed for hazard assessment, we must also establish a degree of consistency in our model’s creation process. Therefore, we will set a fixed seed for reproducibility, which is like marking a specific starting point in our mapping procedure that we can return to. Furthermore, to avoid excessive computational efforts and to operate within a reasonable time frame, we will limit our generation attempts to a cap of 100.

To encapsulate, our assignment is to apply a methodical approach to generate a tree diagram, using a specified function, with 10 industrial facilities as nodes and connections that follow a power-law distribution with an exponent of 3. Remember, we must also ensure the consistency and efficiency of our model by setting a predetermined seed and limiting the number of tries to generate a valid tree.
### Response:
import networkx as nx

# Generate a random powerlaw tree
n = 10  # number of nodes
gamma = 3  # exponent of the power law
seed = 42  # seed for random number generator
tries = 100  # number of attempts to get a valid tree

try:
    tree = nx.random_powerlaw_tree(n, gamma, seed=seed, tries=tries)
    print(nx.info(tree))
except nx.NetworkXError as e:
    print('Failed to generate a valid powerlaw tree:', e)
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