<｜begin▁of▁sentence｜>### Instruction:
As a marketing manager, you're working on a new product launch for a high-tech networking device. You need to develop a strategy to efficiently process the network data and create visual representations of the device's connections. To achieve this, you want to leverage the 'igraph' library and convert a NumPy adjacency matrix—representing the network connections of the device—into a memoryview. This will allow you to dynamically access and manipulate the data before creating an 'igraph' Graph object from it.
### Response:
import igraph as ig
import numpy as np

# Given a NumPy array representing the adjacency matrix of a graph
adj_matrix = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])

# Convert the NumPy array to a contiguous memoryview
memoryview = ig.utils.numpy_to_contiguous_memoryview(adj_matrix)

# Create a graph from the memoryview
graph = ig.Graph.Adjacency(memoryview.tolist())

# Print the summary of the graph
g.summary()
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