The function plot_time_series_outlier in the hana_ml.visualizers.eda module performs OutlierDetectionTS and plots the time series with the highlighted outliers, taking in parameters such as data, column, key, window size, outlier method, threshold, and others, and returns a matplotlib.axes.Axes object or a plotly.graph_objects.Figure object depending on whether plotly is enabled.
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Here is a Python code template based on the provided documentation:

```python
from hana_ml.visualizers.eda import plot_time_series_outlier

# Assuming you have a DataFrame df with columns 'ID' and 'ts'
df = ...

# Basic usage
plot_time_series_outlier(data=df, key='ID', col='ts')

# With additional parameters
plot_time_series_outlier(
    data=df, 
    key='ID', 
    col='ts', 
    window_size=5, 
    outlier_method='z2', 
    threshold=2.5, 
    detect_seasonality=True, 
    alpha=0.3, 
    extrapolation=True, 
    periods=10, 
    random_state=42, 
    n_estimators=200, 
    max_samples=500, 
    bootstrap=True, 
    contamination=0.1, 
    minpts=2, 
    eps=0.7, 
    thread_ratio=0.8, 
    title="Outliers in Time Series", 
    enable_plotly=True
)
```

Please replace `df = ...` with your actual DataFrame. Also, adjust the parameters according to your needs.