<｜begin▁of▁sentence｜>### Instruction:
In the context of evaluating a network visualization software feature, consider a case where we have a software module designed to render visual representations of network connections. This module utilizes a symmetric adjacency matrix to map out the connections between various nodes in the network. Additionally, each node in this network is tagged with metadata that classifies it into specific groups.

For quality assurance purposes, you are tasked with verifying that the software can accurately group and color-code these nodes based on their metadata classification, enhancing the visual analysis of the network. This functionality is expected to be achieved through the 'graspologic.plot.adjplot' function, a crucial component of the visualization feature.

Could you devise a test scenario where you check if the 'graspologic.plot.adjplot' function effectively visualizes the network's adjacency matrix, ensuring that nodes are correctly grouped and color-coded according to their metadata classes? This scenario should be structured in a way that integrates a symmetric adjacency matrix and node metadata classification into the visualization process, as these are fundamental to assessing the software's capability to meet its specifications and quality standards in network visualization tasks.
### Response:
import numpy as np
import pandas as pd
from graspologic.plot import adjplot
import matplotlib.pyplot as plt

# Generate a synthetic adjacency matrix
np.random.seed(2)
adj_matrix = np.random.rand(10, 10)
adj_matrix = (adj_matrix + adj_matrix.T) / 2  # Make it symmetric
data = np.triu(adj_matrix)

# Generate some metadata for the rows and columns
classes = ['A', 'B']
meta_data = pd.DataFrame({'group': np.random.choice(classes, size=10)})

# Plot the adjacency matrix with metadata groups
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
adjplot(data, ax=ax, meta=meta_data, group='group', plot_type='heatmap', palette='Set1', ticks=True)
plt.show()
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