<｜begin▁of▁sentence｜>### Instruction:
In the context of an ongoing legal investigation, where the objective is to scrutinize the interconnected relationships within a group consisting of 4 distinct entities, the request has been made to construct a schematic representation of the said relationships. Specifically, the inquiry pertains to the development of an adjacency matrix that mirrors a network of interactions, characterized by the absence of repetitive engagements between the same entities (self-loops) and inclusive of exactly 6 reciprocal connections (edges). 

Our approach mandates the utilization of a graph model consistent with the Erdos Renyi algorithm, specifically the (n, m) model available within the graspologic library's suite of tools. The finished product will assist the investigating team in evaluating the network's inherent structural elements. How can one effectively employ the (n, m) variant of the Erdos Renyi model provisioned by the graspologic API to curate such an adjacency matrix?
### Response:
import numpy as np
from graspologic.simulations import er_nm

# Set random seed for reproducibility
np.random.seed(1)

# Specify the number of vertices and edges
n = 4
m = 6

# Sample an Erdos Renyi (n, m) graph with specified number of edges
A = er_nm(n, m)

# Output the sampled adjacency matrix
print(A)
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