<｜begin▁of▁sentence｜>### Instruction:
Imagine we're working on a fascinating project within our field of animal behavior, where we're conceptualizing a social network of 100 animal individuals, which we'll construct using the theoretical Erdős-Rényi model. In essence, it's as if we're predicting potential social connections in a population with a set chance of interactions – in this case, a 5% probability that any two given animals might socialize.

Now, to offer a detailed analysis of this synthetic population's social structure, we intend to employ a method reminiscent of the hierarchies observed in animal societies. For this, we will utilize the hierarchical Leiden algorithm, a tool for dissecting intricate social networks like ours into distinct clusters or 'communities' of tightly-knit animal interactions.

Our aim is quite precise: to ensure that each distinct community within our synthetic network is of manageable size – specifically, we want no single group to contain more than 1000 individuals, which in this case is not a concern as our total population is only 100.

And, since our study requires consistency and reproducibility, we've decided that a fixed seed, number 42, will be used, which guarantees that our results can be replicated and verified.
### Response:
import networkx as nx
from graspologic.partition import hierarchical_leiden

# Create a synthetic graph
G = nx.erdos_renyi_graph(n=100, p=0.05)

# Run hierarchical Leiden algorithm
clusters = hierarchical_leiden(
  graph=G,
  max_cluster_size=1000,
  starting_communities=None,
  extra_forced_iterations=0,
  resolution=1.0,
  randomness=0.001,
  use_modularity=True,
  random_seed=42,
  weight_attribute='weight',
  is_weighted=None,
  weight_default=1.0,
  check_directed=True
)

# Output the clusters at each level
for cluster in clusters:
    print(cluster)
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