===== 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 standard library, you use the `statistics.trim_mean` function. 

Note that `trim_mean` takes a `proportiontocut` argument, where `0.1` represents cutting 10% from **each end** of the data (a total of 20% removed).

```python
from statistics import trim_mean

def robust_average(values):
    """
    Returns the 10% trimmed mean of a list of numbers.
    The proportion 0.1 removes 10% from the bottom and 10% from the top.
    """
    return trim_mean(values, 0.1)

# Example usage:
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
result = robust_average(data)
print(f"The 10% trimmed mean is: {result}")
```

### Important details:
1. **Definition of "10% trimmed":** In statistics, "10% trimmed mean" typically means removing 10% from the bottom and 10% from the top (as implemented above). If you meant to remove only 10% total (5% from each side), you would pass `0.05` instead of `0.1`.
2. **Library Requirement:** The `statistics.trim_mean` function was added in **Python 3.4**. If you are using an older version of Python, you would need to implement the sorting and slicing manually.