The chi_squared_goodness_of_fit function in the hana_ml.algorithms.pal.stats module performs the chi-squared goodness-of-fit test on a given data set to determine if an observed distribution differs from an expected chi-squared distribution, returning a comparison between the actual and expected counts and statistical outputs including the calculated chi-squared value, degrees of freedom, and p-value.
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Here is a Python code template for the `chi_squared_goodness_of_fit` function:

```python
from hana_ml import DataFrame
from hana_ml.algorithms.pal.stats import chi_squared_goodness_of_fit

# Assuming that a HANA dataframe 'df' already exists
# df = DataFrame(connection_context, 'your_table')

# Specify the key column
key = 'ID'

# Specify the observed_data and expected_freq columns if they exist
# observed_data = 'your_observed_data_column'
# expected_freq = 'your_expected_freq_column'

# Perform the chi-squared goodness-of-fit test
res, stat = chi_squared_goodness_of_fit(data=df, key=key)

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

Please replace `'your_table'`, `'ID'`, `'your_observed_data_column'`, and `'your_expected_freq_column'` with your actual table name, ID column name, observed data column name, and expected frequency column name respectively. If the `observed_data` and `expected_freq` columns are not given, they default to the first and second non-ID columns respectively.