===== 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 =====
The `statistics.trimmed_mean` function in Python's standard library computes the trimmed mean of a dataset by removing a specified percentage of the data from both ends of the sorted dataset. However, it's important to note that **as of Python 3.8**, the `statistics` module **does not include a `trimmed_mean` function**. It was introduced in **Python 3.10**.

If you're using Python 3.10 or newer, you can use it like this:

```python
import statistics

def robust_average(values):
    return statistics.trimmed_mean(values, 0.10)
```

### Example usage:
```python
values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
print(robust_average(values))  # Output will be the 10% trimmed mean
```

### If you are using Python < 3.10:
You can manually implement the `trimmed_mean` function:

```python
def trimmed_mean(values, proportion):
    sorted_values = sorted(values)
    n = len(sorted_values)
    trim = int(n * proportion)
    trimmed = sorted_values[trim:-trim]
    return sum(trimmed) / len(trimmed)

def robust_average(values):
    return trimmed_mean(values, 0.10)
```

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

Let me know if you'd like a version that handles edge cases (like when `trim` exceeds the length of the list).