The wprec function in the hana_ml.algorithms.pal.tsa.wavelet module is a Python wrapper for the PAL multi-level inverse discrete wavelet transform, which takes a DWT object, an optional wavelet filter, and an optional boundary padding method, and returns a DataFrame of the reconstructed time-series data from the inverse wavelet packet transform.
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Here is a Python code template based on the provided documentation:

```python
from hana_ml.algorithms.pal.tsa.wavelet import wprec

# Assume `dwt` is a `DTW` object with the following attributes:
# dwt.coeff_.collect()
# dwt.stats_.collect()
# dwt.packet

# Specify the wavelet filter and padding method for boundary values
wavelet = 'db1'
boundary = 'zero'

# Reconstruct the original time-series data
rec = wprec(dwt=dwt, wavelet=wavelet, boundary=boundary)

# Collect the reconstructed data
rec_data = rec.collect()

# Print the reconstructed data
print(rec_data)
```

Please replace the `dwt` object with your actual DWT object. The `wavelet` and `boundary` variables are optional and can be omitted if you want to use the values in `dwt.wavelet` and `dwt.boundary` respectively.