===== 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 calculate a 10% trimmed mean using the Python `statistics` module, you can use the `statistics.trimmed_mean` function. This function requires a `proportiontocut` argument, which represents the fraction of data to remove from each end of the sorted dataset.

For a **10% trimmed mean**, you cut 10% from the bottom and 10% from the top, so the `proportiontocut` is `0.1`.

Here is the implementation:

```python
import statistics

def robust_average(values):
    """
    Returns the 10% trimmed mean of a list of numbers.
    """
    return statistics.trimmed_mean(values, proportiontocut=0.1)

# Example usage:
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100]
result = robust_average(data)

print(f"Original data: {data}")
print(f"10% Trimmed mean: {result}")
```

### Key details:
*   **`proportiontocut=0.1`**: This tells the function to remove 10% of the observations from the lower end and 10% from the upper end.
*   **Requirement**: This function was introduced in **Python 3.4**. If you are using an older version of Python, you would need to calculate this manually by sorting the list and slicing it.
*   **Behavior**: If the number of items to be removed is not an integer, the `statistics` module handles the rounding internally to determine exactly how many elements are trimmed.