<｜begin▁of▁sentence｜>### Instruction:
In the pursuit of acquiring funding for our next groundbreaking ecological study, we are looking to model the interconnectedness of diverse species within a simulated environment. The model I'm proposing should reflect the intricacy of natural systems, mirroring a power law in the distribution of connections amongst species—envisioning each species as a node within this network.

To construct an accurate representation, we aim to develop a tree-like structure encompassing 10 distinct species nodes. The relational dynamics between these species must adhere to a power law with an exponent value of 3, reflecting real-world biological networks where few species have numerous connections, while the majority have far fewer.

For the initial simulations, I plan to use NetworkX, a reputable Python library favored amongst our analytical team, which allows for the construction of complex networks. The model's reproducibility is crucial for peer verification, for which we require the random number generation to be consistent. Hence, we will use a predetermined random seed of 42.

The robustness of the model is also key. As such, I propose we iteratively refine our constructed tree, allowing up to 100 attempts to ensure that the resulting network is a tree and adheres to the desired degree distribution.

I must include this complex network model within our grant proposal under the methods section, establishing the sophistication and thoroughness of our research strategy. Could you assist in the procedural documentation, ensuring that we correctly leverage the `random_powerlaw_tree` function from NetworkX to achieve this? 

Your input will be invaluable in fortifying our proposal's technical appendices and ultimately enhancing our chances of securing the necessary funding for our research endeavors.
### Response:
import networkx as nx

# Parameters
n = 10  # number of nodes
gamma = 3  # exponent of the power law
seed = 42  # seed for random number generation
tries = 100  # number of attempts to get a valid sequence

# Generate a tree with a power law degree distribution
try:
    tree = nx.random_powerlaw_tree(n, gamma, seed=seed, tries=tries)
    print('Generated tree:', tree.edges())
except nx.NetworkXError as e:
    print('An error occurred:', e)
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