===== 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 =====
In Python's `statistics` module, the `trimmed_mean` function requires two arguments: the data and the proportion of data to cut from each end. 

To calculate a **10% trimmed mean**, you must specify a proportion of `0.1`.

Here is the implementation:

```python
from statistics import trimmed_mean

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

# Example usage:
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
print(robust_average(data)) 
# Output: 5.5
```

### Explanation:
*   **`trimmed_mean(data, proportion)`**: The `proportion` argument represents the fraction of data to remove from **each end** of the sorted dataset. 
*   **10% trimming**: Setting the proportion to `0.1` means the function will remove the lowest 10% and the highest 10% of the values before calculating the arithmetic mean of the remainder.
*   **Note**: This function was introduced in **Python 3.9**. If you are using an older version of Python, you would need to calculate this manually using `sorted()` and slicing.