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

Here’s how you can do it:

### ✅ 10% Trimmed Mean Definition:
- Sort the list of numbers.
- Remove the lowest 10% and highest 10% of the data.
- Compute the mean of the remaining values.

---

### ✅ Implementation:

```python
def robust_average(values):
    if not values:
        return 0  # or raise an error, depending on your needs

    # Sort the list
    sorted_values = sorted(values)
    n = len(sorted_values)

    # Calculate the number of values to trim
    trim_percent = 0.10
    trim_count = int(n * trim_percent)

    # Trim the list
    trimmed = sorted_values[trim_count:-trim_count]

    # Return the mean of the trimmed list
    return sum(trimmed) / len(trimmed)
```

---

### ✅ Example Usage:

```python
values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 200]
print(robust_average(values))  # Output: ~5.5
```

---

### 🔍 Notes:
- If the list has fewer than 10 values, trimming 10% might result in an empty list. You may want to handle such edge cases.
- This implementation is **not using** the `statistics.trimmed_mean` function (which doesn't exist in the standard library), but it **mimics** the behavior of a 10% trimmed mean.

Let me know if you want to use a third-party library like `scipy` for a more robust implementation.