<｜begin▁of▁sentence｜>### Instruction:
Imagine our team is embarking on a new project designed to optimize network performance within our company's IT infrastructure. As part of our preliminary system analysis, we've been given the responsibility of evaluating our current network topology to identify potential areas for enhancement. The data we have at our disposal is an adjacency matrix stored as a SciPy sparse array. This matrix is a digital representation of our network's connections, where each non-diagonal zero entry signifies the absence of a connection between two points (or nodes) in the network, and any non-zero entry indicates a direct link between them. Notably, the matrix is symmetric, reflecting the undirected nature of our network, with no self-connections as indicated by zeros along the diagonal.

Our immediate task is to create a tangible model of our network from this abstract representation to detail its current configuration clearly. Specifically, we are tasked with employing NetworkX, a powerful network analysis tool, to construct an undirected graph that mirrors our adjacency matrix. The conversion process must accurately capture the network's structure, representing each network point as a node within the graph and each connection as an edge.

Upon successful construction of this graph, we should be able to enumerate its constituents, providing us with a comprehensive list of all nodes and the edges that connect them. This visual and enumerative breakdown will serve as the foundation for our subsequent analysis phase, where we'll identify key areas for network optimization and enhancement.

In doing so, we aim to bridge the gap between the current state of our network infrastructure and the ideal configuration that optimizes performance and reliability. This step is critical in our overall project objective of enhancing our company's IT systems to support business needs more effectively, aligning with our role in evaluating and proposing system improvements for optimal performance and usability.
### Response:
import networkx as nx
import scipy.sparse as sp

# Creating a SciPy sparse array to represent an adjacency matrix
A = sp.sparse.csr_matrix([[0, 1], [1, 0]])

# Using the from_scipy_sparse_array function to create a NetworkX graph from the adjacency matrix
G = nx.from_scipy_sparse_array(A)

# Displaying nodes and edges of the graph
print('Nodes:', G.nodes())
print('Edges:', G.edges())
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