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.

model_manager/workflows/cleanup_files.py

@@ -13,7 +13,7 @@ with workflow.unsafe.imports_passed_through():
     from typing import Any
 
     from model_manager.activities.activities import Activities
-    from model_manager.workflows.train_model import POD_ID, no_retry_policy
+    from model_manager.workflows.train_model import no_retry_policy
 
     TIMEOUT_CLEANUP_LOCAL = int(os.getenv('TIMEOUT_CLEANUP_LOCAL', '120'))
 
@@ -45,7 +45,7 @@ class CleanupFiles:
         # Metadata for tracking
         metadata = {
             'metadata': {
-                'pod_id': POD_ID,
+                'pod_id': os.getenv('POD_ID'),
                 'workflow_name': 'cleanup_files',
             }
         }