Update TODOs [ci skip]

TODOs

@@ -20,12 +20,17 @@ coscine_watchdog/coscine_client.py:185:    # TODO use update file rath then uplo
 coscine_watchdog/coscine_client.py:220:        ("datatype", "Datatype is missing"),  # , # TODO
 coscine_watchdog/coscine_client.py:221:        # ('dataset_type', 'Dataset type is missing') # TODO
 docs/index.rst:4:.. TODO change to two (or more) main pages + metadata. one on data generation one on ai. 
+dynamics_learning/benchmark_number_of_runs.py:211:        # TODO load ITA model instead of dummy model
+dynamics_learning/benchmark_number_of_runs.py:240:        # TODO load LLT Data
+dynamics_learning/benchmark_number_of_runs.py:241:        # TODO load ITA model
+dynamics_learning/benchmark_number_of_runs.py:242:        # TODO set hyperparameters for LLT model based on ITA model with known hyperparameters
+dynamics_learning/benchmark_number_of_runs.py:256:        # TODO load LLT Data
+dynamics_learning/benchmark_number_of_runs.py:257:        # TODO load foundation model
+dynamics_learning/benchmark_number_of_runs.py:258:        # TODO set hyperparameters for LLT model based on foundation model
 dynamics_learning/dynamics_learning/testing/__init__.py:256:        # somewhere FIXME: Daten scheinen zu kurz zu sein?
 dynamics_learning/dynamics_learning/testing/__init__.py:640:        # somewhere FIXME: Daten scheinen zu kurz zu sein?
-dynamics_learning/dynamics_learning/training/__init__.py:169:    )  # TODO implement another naming schema, as soon as a custom application profiles for the models is available in coscine.
-dynamics_learning/dynamics_learning/training/__init__.py:175:        f"{decision_metric}_val_loss"  # TODO implement custom coscine application profile for the models
-dynamics_learning/foundation_model.py:75:            test_dataset.download()  # FIXME
-dynamics_learning/foundation_model.py:77:            model_analysis(test_dataset, model)  # FIXME
+dynamics_learning/dynamics_learning/training/__init__.py:265:    )  # TODO implement another naming schema, as soon as a custom application profiles for the models is available in coscine.
+dynamics_learning/dynamics_learning/training/__init__.py:271:        f"{decision_metric}_val_loss"  # TODO implement custom coscine application profile for the models based on MITM base profile
 franka_lock_unlock/README.rst:1:.. TODO describe docker compose up for individual component
 src_jan/ROS/package.xml:16:  <license>TODO</license>
 src_jan/ROS/panda_auto_dynamics.py:5:# TODO DOCSTRING DOCUMENTATION