The mcmc function in the hana_ml.algorithms.pal.random module generates samples of a specified distribution using Markov chain Monte Carlo simulation, with various parameters to customize the simulation such as the number of iterations, seed for the random number generator, and parameters specific to the chosen distribution.
------
Here is a Python code template based on the provided help documentation:

```python
from hana_ml.algorithms.pal.random import mcmc
from hana_ml import dataframe

# Create a connection to HANA database
cc = dataframe.ConnectionContext('address', port, 'username', 'password')

# Specify the distribution and its parameters
distribution = 'student_t'
mu = 0
sigma = 1
nu = 1
chain_iter = 50
thin = 10
init_radius = 0

# Generate MCMC samples from the specified distribution
res = mcmc(cc, distribution=distribution, mu=mu, sigma=sigma, nu=nu, chain_iter=chain_iter, thin=thin, init_radius=init_radius)

# Collect the results
df = res.collect()
print(df)
```

Please replace `'address'`, `port`, `'username'`, and `'password'` with your actual HANA database connection details.