<｜begin▁of▁sentence｜>### Instruction:
Imagine we're looking at a forest ecosystem, where different species of trees are our nodes, and we use the density of inter-species interactions to reflect the strength of the ecological relationship between them. This is much like a weighted network between trees, where the weights symbolize how strongly each pair of species is linked through ecological processes.

Now, since we aim to manage and protect this forest in a balanced way, it would be helpful to standardize these interaction strengths so that we can more accurately compare how different species pairs interact within this network. We could do this by recalibrating the weights of these relationships in a way that ranks them from the weakest link to the strongest.

Translating this into our forestry language: How can we apply the 'simple-nonzero' method from the 'pass_to_ranks' API within graspologic to adjust these weights of our ecological network, ensuring that we better understand the relative importance of each interaction without changing the underlying structure of our forest's complex web of life?
### Response:
import networkx as nx
from graspologic.utils import pass_to_ranks

G = nx.Graph()
G.add_edges_from([(1, 2, {'weight': 3}), (1, 3, {'weight': 2}), (2, 4, {'weight': 4}), (3, 4, {'weight': 1})])

# Apply pass to ranks
ranked_graph = pass_to_ranks(G, method='simple-nonzero')

print(ranked_graph)
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