feat: enhance training and experiment tracking functionality

- Updated `Activities` class to improve garbage collection handling.
- Enhanced error messaging in `ExperimentTracking` for better clarity on update failures.
- Refactored `Training` class to streamline exception handling and improve type hints.
- Introduced new methods in `TrainModelParams` for better handling of experiment run IDs and model metadata.
- Added functionality to extract model equations in `DataManagerRepository` for linear regression models.

tests/workflows/test_cleanup_files.py

@@ -1,5 +1,6 @@
 """Unit tests for the CleanupFiles workflow."""
 
+import os
 from unittest.mock import AsyncMock, patch
 
 import pytest
@@ -7,7 +8,6 @@ import pytest
 
 @pytest.mark.asyncio
 @patch('model_manager.workflows.cleanup_files.workflow')
-@patch('model_manager.workflows.cleanup_files.POD_ID', 'temporal-pod')
 async def test_cleanup_files_workflow(mock_workflow_module):
     """Test the CleanupFiles workflow."""
     from model_manager.workflows.cleanup_files import CleanupFiles
@@ -17,7 +17,8 @@ async def test_cleanup_files_workflow(mock_workflow_module):
 
     # Instantiate and run the workflow
     workflow_instance = CleanupFiles()
-    await workflow_instance.run({})
+    with patch.dict(os.environ, {'POD_ID': 'temporal-pod'}):
+        await workflow_instance.run({})
 
     # Verify that the activities were called with the correct parameters
     calls = mock_workflow_module.execute_activity_method.call_args_list