The OutlierProfiling class in the hana_ml.algorithms.pal.outlier_profiling module provides a way to detect outliers in a dataset from multiple perspectives using various hana-ml classes/functions for outlier detection, each with its own parameter specification.
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Here is a Python code template for the `OutlierProfiling` class:

```python
from hana_ml.algorithms.pal.outlier_profiling import OutlierProfiling

# Create an instance of the OutlierProfiling class
profiling = {
    'KMeansOutlier': {'n_clusters': 3, 'distance_level': 2, 'contamination': 0.1},
    'grubbs_test': {'method': 'two-tailed', 'alpha': 0.05},
    'iqr': {'multiplier': 1.5},
    'DBSCAN': {'minpts': 5, 'eps': 0.5},
    'OneClassSVM': {'c': 1, 'kernel': 'rbf', 'degree': 3, 'gamma': 0.1, 'coef_lin': 0, 'coef_const': 1, 'shrink': True, 'tol': 0.001, 'nu': 0.5, 'scale_info': 'standard', 'handle_missing': 'mean'},
    'IsolationForest': {'n_estimators': 100, 'max_samples': 'auto', 'max_features': 1.0, 'bootstrap': False, 'random_state': None, 'contamination': 0.1}
}
outlier_profiling = OutlierProfiling(profiling=profiling)

# Fit and predict outliers
data = # DataFrame containing the data used for outlier detection
key = # Name of ID column in data
categorical_variable = # Names of integer columns in data that should be treated as categorical
string_variable = # Name of string column storing not categorical data
grubbs_cols = # Numerical columns used for Grubbs' test
iqr_cols = # Numerical columns used for Inter-Quantile-Range(IQR) test
outliers = outlier_profiling.fit_predict(data, key=key, categorical_variable=categorical_variable, string_variable=string_variable, grubbs_cols=grubbs_cols, iqr_cols=iqr_cols)

# Print the detected outliers
for algorithm, outliers_df in outliers.items():
    print(f"Outliers detected by {algorithm}:")
    print(outliers_df)
```

Please replace the `# DataFrame containing the data used for outlier detection` comment with your actual DataFrame. Also, replace the `# Name of ID column in data`, `# Names of integer columns in data that should be treated as categorical`, `# Name of string column storing not categorical data`, `# Numerical columns used for Grubbs' test`, and `# Numerical columns used for Inter-Quantile-Range(IQR) test` comments with your actual column names.