The HANAGeneratorForCAP class in the hana_ml.artifacts.generators.hana module is a HANA artifacts generator for existing CAP projects, which takes a project name, output directory, and optional namespace as parameters, and includes methods to generate CAP artifacts and create an input table for the input dataframe.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.artifacts.generators.hana import HANAGeneratorForCAP
from hana_ml.algorithms.pal.trees import HybridGradientBoostingClassifier
from hana_ml.algorithms.pal.decomposition import PCA
from hana_ml.dataframe import DataFrame

# Assuming diabetes_train and diabetes_test_m are DataFrame objects
# diabetes_train = DataFrame(...)
# diabetes_test_m = DataFrame(...)

# Define the pipeline
my_pipeline = Pipeline([
    ('PCA', PCA(scaling=True, scores=True)),
    ('HGBT_Classifier', HybridGradientBoostingClassifier(
        n_estimators=4, split_threshold=0,
        learning_rate=0.5, fold_num=5,
        max_depth=6))])

# Fit and predict
my_pipeline.fit(diabetes_train, key="ID", label="CLASS")
my_pipeline.predict(diabetes_test_m, key="ID")

# Initialize HANAGeneratorForCAP
hanagen = HANAGeneratorForCAP(project_name="my_proj",
                              output_dir=".",
                              namespace="hana.ml")

# Generate artifacts
hanagen.generate_artifacts(my_pipeline)

# Materialize data
hanagen.materialize_ds_data(to_materialize=True)
```

Please replace the placeholders with your actual data and parameters.