===== 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` module in Python's standard library does **not** include a `trimmed_mean` function. However, you can implement a `robust_average` function that computes the **10% trimmed mean** manually.

A **10% trimmed mean** is calculated by:

1. Sorting the list of numbers.
2. Removing the lowest 10% and highest 10% of the values.
3. Calculating the mean of the remaining values.

Here's how you can implement this:

```python
import statistics

def robust_average(values):
    if not values:
        return None  # or raise an error if needed

    n = len(values)
    trim_percent = 0.10
    trim_count = int(n * trim_percent)

    # Sort the list
    sorted_values = sorted(values)

    # Remove the lowest and highest 10% of values
    trimmed_values = sorted_values[trim_count:-trim_count]

    # Return the mean of the trimmed values
    return statistics.mean(trimmed_values)
```

### Example Usage:

```python
data = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
print(robust_average(data))  # Output: 55.0
```

### Notes:
- If the list length is small, the `trim_count` may be zero or even negative, so you might want to handle edge cases more carefully (e.g., using `max(0, trim_count)`).
- This function does **not** use the `statistics.trimmed_mean` function, which is not available in the standard library.