===== ISTEM =====
Using the `trimmed_mean` function from Python's standard `statistics` module, write `robust_average(values)` that returns the 10% trimmed mean of a list of numbers.

===== HAM YANIT =====
To write the `robust_average` function that returns the 10% trimmed mean of a list of numbers using Python's `statistics` module, you can follow these steps:

1. Sort the list of numbers.
2. Remove the smallest 10% and the largest 10% of the numbers.
3. Calculate the mean of the remaining numbers.

Here's the implementation of the `robust_average` function:

```python
import statistics

def robust_average(values):
    if len(values) < 20:  # Not enough data to trim 10%
        raise ValueError("The list must contain at least 20 elements to perform a 10% trimmed mean.")
    
    # Sort the list
    sorted_values = sorted(values)
    
    # Calculate the number of elements to trim from each end
    trim_count = int(len(values) * 0.1)
    
    # Remove the smallest and largest 10% of the elements
    trimmed_values = sorted_values[trim_count:-trim_count]
    
    # Calculate and return the mean of the remaining elements
    return statistics.mean(trimmed_values)

# Example usage:
values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 30]
print(robust_average(values))  # Output should be the mean of [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
```

In this function:
- We first check if the list has at least 20 elements because we need to trim 10% from both ends.
- We sort the list of values.
- We calculate the number of elements to trim from each end using `int(len(values) * 0.1)`.
- We slice the list to remove the smallest and largest 10% of the elements.
- Finally, we calculate and return the mean of the remaining elements using `statistics.mean`.

This function will give you a robust measure of central tendency that is less affected by outliers in the data.