The KBinsDiscretizer class in the hana_ml.algorithms.pal.preprocessing module is used to bin continuous data into a number of intervals and perform local smoothing, with the output data type being the same as the input data type.
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Here is a Python code template for the `KBinsDiscretizer` class:

```python
from hana_ml.algorithms.pal.preprocessing import KBinsDiscretizer

# Create a KBinsDiscretizer instance
binning = KBinsDiscretizer(strategy='uniform_size', smoothing='means', bin_size=10)

# Assuming that df1 is your input DataFrame
# Perform fit() on the given DataFrame
binning.fit(data=df1, key='ID')

# Assuming that df2 is your DataFrame for transforming
# Perform transform() on the given DataFrame
result = binning.transform(data=df2, key='ID')

# Print the result
print(result.collect())
```

Please replace `df1` and `df2` with your actual DataFrames. The `key` parameter should be the name of the ID column in your DataFrame.