<｜begin▁of▁sentence｜>### Instruction:
Imagine you are designing an intuitive interface to help a data analyst showcase a visualization. They have a grid — a symmetrical 10x10 matrix — brimming with similarity scores. These aren't just any numbers; each corresponds to how closely related two of the items are. There’s a twist, though: every item is tagged with dual identities, one aligning it to either group 'A' or 'B' horizontally, and another marking it 'X' or 'Y' vertically.

The task at hand? Craft the interface so that it can wield the matrixplot function from graspologic, turning this numerical tapestry into a striking visual sorted by those nuanced group tags. The aim is a graphic that readily divulges patterns, with group-labeled and color-coded borders for that added layer of understanding. The design challenge is to ensure that within the visualization, the rows and columns are systematically organized by their metadata groups — and that those vast or minuscule group sizes don't go unnoticed, dictating the hierarchy of the display.

How can you infuse such specificity into the UI, empowering the matrixplot tool to not just display these relationships but also articulate the group-based rhythms through a meticulous, hierarchy-conscious arrangement?
### Response:
import numpy as np
import pandas as pd
import seaborn as sns
from graspologic.plot import matrixplot
import matplotlib.pyplot as plt

# Create a 10x10 random matrix
np.random.seed(42)
data = np.random.rand(10, 10)

# Create metadata for rows and columns
row_meta = pd.DataFrame({'group': np.random.choice(['A', 'B'], size=10)})
col_meta = pd.DataFrame({'group': np.random.choice(['X', 'Y'], size=10)})

# Plot the matrix with metadata
fig, ax = plt.subplots(figsize=(10, 10))
matrixplot(data,
           ax=ax,
           row_meta=row_meta,
           col_meta=col_meta,
           plot_type='heatmap',
           row_group='group',
           col_group='group',
           row_color='group',
           col_color='group',
           row_group_order='size',
           col_group_order='size',
           cmap='RdBu_r')
plt.show()
<|EOT|>
