refactor: updated OpenMLEvaluation to use dataclass decorator (#1559)

I have Refactored the `OpenMLEvaluation` class from a traditional Python class to use the `@dataclass` decorator to reduce boilerplate code and improve code maintainability.

#### Metadata
* Reference Issue: #1540 
* New Tests Added: No
* Documentation Updated: No
* Change Log Entry: Refactored the `OpenMLEvaluation` class to use the `@dataclass`

#### Details 
Edited the `OpenMLEvaluation` class in `openml\evaluations\evaluation.py` to use `@dataclass` decorator. This significantly reduces the boilerplate code in the following places:

- Instance Variable Definitions

**Before:**
```python
def __init__(
    self,
    run_id: int,
    task_id: int,
    setup_id: int,
    flow_id: int,
    flow_name: str,
    data_id: int,
    data_name: str,
    function: str,
    upload_time: str,
    uploader: int,
    uploader_name: str,
    value: float | None,
    values: list[float] | None,
    array_data: str | None = None,
):
    self.run_id = run_id
    self.task_id = task_id
    self.setup_id = setup_id
    self.flow_id = flow_id
    self.flow_name = flow_name
    self.data_id = data_id
    self.data_name = data_name
    self.function = function
    self.upload_time = upload_time
    self.uploader = uploader
    self.uploader_name = uploader_name
    self.value = value
    self.values = values
    self.array_data = array_data
```

**After:**
```python
run_id: int
task_id: int
setup_id: int
flow_id: int
flow_name: str
data_id: int
data_name: str
function: str
upload_time: str
uploader: int
uploader_name: str
value: float | None
values: list[float] | None
array_data: str | None = None
```

-  _to_dict Method Simplification

**Before:**
```python
def _to_dict(self) -> dict:
    return {
        "run_id": self.run_id,
        "task_id": self.task_id,
        "setup_id": self.setup_id,
        "flow_id": self.flow_id,
        "flow_name": self.flow_name,
        "data_id": self.data_id,
        "data_name": self.data_name,
        "function": self.function,
        "upload_time": self.upload_time,
        "uploader": self.uploader,
        "uploader_name": self.uploader_name,
        "value": self.value,
        "values": self.values,
        "array_data": self.array_data,
    }
```

**After:**
```python
def _to_dict(self) -> dict:
    return asdict(self)
```
All tests are passing with accordnce to the changes:

```bash
PS C:\Users\ASUS\Documents\work\opensource\openml-python> pytest tests/test_evaluations/
======================================= test session starts =======================================
platform win32 -- Python 3.14.0, pytest-9.0.2, pluggy-1.6.0
rootdir: C:\Users\ASUS\Documents\work\opensource\openml-python
configfile: pyproject.toml
plugins: anyio-4.12.0, flaky-3.8.1, asyncio-1.3.0, cov-7.0.0, mock-3.15.1, rerunfailures-16.1, timeout-2.4.0, xdist-3.8.0, requests-mock-1.12.1
asyncio: mode=Mode.STRICT, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function
collected 13 items                                                                                 

tests\test_evaluations\test_evaluation_functions.py ............                             [ 92%]
tests\test_evaluations\test_evaluations_example.py .                                         [100%]

================================= 13 passed in 274.80s (0:04:34) ================================== 
```

Author: rohansen856

openml/evaluations/evaluation.py

@@ -1,15 +1,16 @@
 # License: BSD 3-Clause
 from __future__ import annotations
 
+from dataclasses import asdict, dataclass
+
 import openml.config
 import openml.datasets
 import openml.flows
 import openml.runs
 import openml.tasks
 
 
-# TODO(eddiebergman): A lot of this class is automatically
-# handled by a dataclass
+@dataclass
 class OpenMLEvaluation:
     """
     Contains all meta-information about a run / evaluation combination,
@@ -48,55 +49,23 @@ class OpenMLEvaluation:
         (e.g., in case of precision, auroc, recall)
     """
 
-    def __init__(  # noqa: PLR0913
-        self,
-        run_id: int,
-        task_id: int,
-        setup_id: int,
-        flow_id: int,
-        flow_name: str,
-        data_id: int,
-        data_name: str,
-        function: str,
-        upload_time: str,
-        uploader: int,
-        uploader_name: str,
-        value: float | None,
-        values: list[float] | None,
-        array_data: str | None = None,
-    ):
-        self.run_id = run_id
-        self.task_id = task_id
-        self.setup_id = setup_id
-        self.flow_id = flow_id
-        self.flow_name = flow_name
-        self.data_id = data_id
-        self.data_name = data_name
-        self.function = function
-        self.upload_time = upload_time
-        self.uploader = uploader
-        self.uploader_name = uploader_name
-        self.value = value
-        self.values = values
-        self.array_data = array_data
+    run_id: int
+    task_id: int
+    setup_id: int
+    flow_id: int
+    flow_name: str
+    data_id: int
+    data_name: str
+    function: str
+    upload_time: str
+    uploader: int
+    uploader_name: str
+    value: float | None
+    values: list[float] | None
+    array_data: str | None = None
 
     def _to_dict(self) -> dict:
-        return {
-            "run_id": self.run_id,
-            "task_id": self.task_id,
-            "setup_id": self.setup_id,
-            "flow_id": self.flow_id,
-            "flow_name": self.flow_name,
-            "data_id": self.data_id,
-            "data_name": self.data_name,
-            "function": self.function,
-            "upload_time": self.upload_time,
-            "uploader": self.uploader,
-            "uploader_name": self.uploader_name,
-            "value": self.value,
-            "values": self.values,
-            "array_data": self.array_data,
-        }
+        return asdict(self)
 
     def __repr__(self) -> str:
         header = "OpenML Evaluation"
@@ -119,11 +88,12 @@ def __repr__(self) -> str:
         }
 
         order = [
-            "Uploader Date",
+            "Upload Date",
             "Run ID",
             "OpenML Run URL",
             "Task ID",
-            "OpenML Task URL" "Flow ID",
+            "OpenML Task URL",
+            "Flow ID",
             "OpenML Flow URL",
             "Setup ID",
             "Data ID",