The function plot_pacf in the hana_ml.visualizers.eda module plots the partial autocorrelation function (PACF) of a given time series data from a HANA DataFrame, with options to specify the ID column, calculation method, maximum lag, whether to calculate confidence intervals, and whether to use plotly instead of matplotlib for plotting.
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Here is the executable code template for the `plot_pacf` function:

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

# Assuming that 'df' is your HANA DataFrame
# 'ID' is the ID column and 'ts' is the time series data column

# Using matplotlib
ax = plot_pacf(data=df, key='ID', col='ts', method='fft', thread_ratio=0.4, max_lag=20, calculate_confint=True)

# Using plotly
fig = plot_pacf(data=df, key='ID', col='ts', method='fft', thread_ratio=0.4, max_lag=20, calculate_confint=True, enable_plotly=True)
```

Please replace `'df'`, `'ID'`, and `'ts'` with your actual DataFrame and column names. You can also adjust the other parameters as needed.