<｜begin▁of▁sentence｜>### Instruction:
Certainly! Let's put this into a scenario befitting a Training Coordinator's perspective:

"Imagine you're overseeing a training program aimed at enhancing the network analysis skills of a group of employees. As part of the practical exercises, the team has been provided with an adjacency matrix representing a network where the edge weights signify the intensity of connectivity between different nodes—or skills, for illustrative purposes. To progress with the training, you're interested in standardizing these connection strengths so comparisons can be more straightforward. Specifically, you'd like to employ the 'pass_to_ranks' method from the 'graspologic.utils' API to adjust these values by their standing or 'rank' within the entire network matrix—effectively leveling the playing field and assessing the connections on a relative scale. How would you reconfigure the original matrix to reflect these rank-based weights, ensuring that the learning materials include exercises that reflect these rescaled network dynamics?"
### Response:
import numpy as np
import graspologic.utils

# Given an adjacency matrix for a graph
adjacency_matrix = np.array([
    [0, 1, 3],
    [1, 0, 2],
    [3, 2, 0]
])

# Rescale edge weights based on their relative rank
ptr_matrix = graspologic.utils.pass_to_ranks(adjacency_matrix)

# Print the resulting pass-to-ranks matrix
print(ptr_matrix)
<|EOT|>
