The MetricsVisualizer class in the hana_ml.visualizers.metrics module is used to visualize metrics with customizable parameters such as axes, size, color map, title, and the option to use plotly instead of matplotlib, and it includes methods to plot a confusion matrix, reset, and set axes, colormap, and size.
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Here is a Python code template based on the provided documentation:

```python
import hana_ml.visualizers.metrics as metrics
import matplotlib.pyplot as plt

# Create a matplotlib Axes
ax = plt.gca()

# Create a MetricsVisualizer
visualizer = metrics.MetricsVisualizer(ax=ax, size=(800, 600), cmap='viridis', title='Confusion Matrix', enable_plotly=False)

# Plot the confusion matrix
visualizer.plot_confusion_matrix(df, normalize=True)

# Reset the visualizer
visualizer.reset()

# Set the Axes
visualizer.set_ax(ax)

# Set the colormap
visualizer.set_cmap('plasma')

# Set the size
visualizer.set_size((800, 600))

# Get the matplotlib Axes
ax = visualizer.ax

# Get the color map
cmap = visualizer.cmap

# Get the size
size = visualizer.size
```

Please replace the DataFrame `df` with your actual data. The `CLASS` and `''` columns should match your data's columns.