<｜begin▁of▁sentence｜>### Instruction:
As a property manager for a real estate firm, you may be tasked with analyzing the network of relationships between different properties and their attributes. Imagine you have been given a weighted adjacency matrix representing the connections between various properties in your portfolio, with the weights indicating the strength of relationships. Your goal is to transform this matrix into a directed graph for better visualization and analysis.

To accomplish this, you can utilize the igraph library in Python, which provides a straightforward way to create and analyze graphs. By using the igraph library, you can convert the weighted adjacency matrix into a directed graph and then print a summary of its properties to gain insights into the network structure.
### Response:
import igraph as ig
import numpy as np

# Define a numpy 2D array for the weighted adjacency matrix
weighted_adj_matrix = np.array([[0, 1, 2], [1, 0, 0], [2, 0, 0]])

# Create a directed graph from the weighted adjacency matrix
graph = ig.Graph.Weighted_Adjacency(weighted_adj_matrix.tolist(), mode='directed', attr='weight', loops=False)

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