The KDE class in the hana_ml.algorithms.pal.kernel_density module is used to perform Kernel Density Estimation, a method for smoothing a histogram, with various parameters to customize the process such as thread ratio, leaf size, kernel function type, search method, bandwidth, and distance level.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.kernel_density import KDE

# Define the parameters for the KDE instance
thread_ratio = 0.0
leaf_size = 30
kernel = 'gaussian'
method = 'brute_force'
distance_level = 'euclidean'
minkowski_power = 3.0
atol = 0
rtol = 1e-8
bandwidth = 0
resampling_method = 'loocv'
evaluation_metric = 'nll'
bandwidth_values = None
bandwidth_range = None
stat_info = True
random_state = 0
search_strategy = 'grid'
repeat_times = 1
algorithm = 'brute-force'

# Create a KDE instance
kde = KDE(thread_ratio=thread_ratio, leaf_size=leaf_size, kernel=kernel, method=method, distance_level=distance_level, minkowski_power=minkowski_power, atol=atol, rtol=rtol, bandwidth=bandwidth, resampling_method=resampling_method, evaluation_metric=evaluation_metric, bandwidth_values=bandwidth_values, bandwidth_range=bandwidth_range, stat_info=stat_info, random_state=random_state, search_strategy=search_strategy, repeat_times=repeat_times, algorithm=algorithm)

# Assume df_train and df_pred are the training and prediction dataframes respectively
# Fit the model
kde.fit(data=df_train, key='ID')

# Predict
res, stats = kde.predict(data=df_pred, key='ID')

# Print the results
print(res.collect())
print(stats.collect())
```

Please replace `df_train` and `df_pred` with your actual dataframes.