diff --git a/_toc.yml b/_toc.yml
index e24ca7753..6bb51801f 100644
--- a/_toc.yml
+++ b/_toc.yml
@@ -1,10 +1,12 @@
 format: jb-book
 root: README
+
 parts:
 - caption: Getting Started
   chapters:
   - file: docs/UseOverviewGuide
   - file: docs/course
+
 - caption: Installation
   chapters:
   - file: docs/installation
@@ -31,40 +33,41 @@ parts:
   - file: docs/pytorch/user_guide.md
   - file: docs/pytorch/pytorch_config.md
   - file: docs/pytorch/architectures.md
+
 - caption: Quick Start Tutorials
   chapters:
   - file: docs/quick-start/single_animal_quick_guide
   - file: docs/quick-start/tutorial_maDLC
-- caption: 🚀 Beginner's Guide to DeepLabCut
+
+- caption: "🚀 Beginner's Guide to DeepLabCut"
   chapters:
   - file: docs/beginner-guides/beginners-guide
   - file: docs/beginner-guides/manage-project
   - file: docs/beginner-guides/labeling
   - file: docs/beginner-guides/Training-Evaluation
   - file: docs/beginner-guides/video-analysis
-- caption: 🚀 Main Demo Notebooks
+
+- caption: "🚀 Main Demo Notebooks"
   chapters:
   - file: examples/COLAB/COLAB_DEMO_SuperAnimal
   - file: examples/COLAB/COLAB_DEMO_mouse_openfield
   - file: examples/COLAB/COLAB_3miceDemo
   - file: examples/COLAB/COLAB_HumanPose_with_RTMPose
 
-
-
-- caption: 🚀 Notebooks For Your Data
+- caption: "🚀 Notebooks For Your Data"
   chapters:
   - file: examples/COLAB/COLAB_YOURDATA_SuperAnimal
   - file: examples/COLAB/COLAB_YOURDATA_TrainNetwork_VideoAnalysis
   - file: examples/COLAB/COLAB_YOURDATA_maDLC_TrainNetwork_VideoAnalysis
 
-- caption: 🚀 Special Feature Demos
+- caption: "🚀 Special Feature Demos"
   chapters:
   - file: examples/COLAB/COLAB_transformer_reID
   - file: examples/COLAB/COLAB_BUCTD_and_CTD_tracking
   - file: examples/JUPYTER/Demo_3D_DeepLabCut
   - file: examples/COLAB/COLAB_DLC_ModelZoo
 
-- caption: 🧑‍🍳 Cookbook (detailed helper guides)
+- caption: "🧑‍🍳 Cookbook (detailed helper guides)"
   chapters:
   - file: docs/convert_maDLC
   - file: docs/recipes/OtherData
@@ -82,6 +85,7 @@ parts:
 - caption: Hardware Tips
   chapters:
   - file: docs/recipes/TechHardware
+
 - caption: DeepLabCut-Live!
   chapters:
   - file: docs/dlc-live/deeplabcutlive
@@ -110,7 +114,7 @@ parts:
   - file: docs/benchmark
   - file: docs/pytorch/Benchmarking_shuffle_guide
 
-- caption: Mission & Contribute
+- caption: "Mission & Contribute"
   chapters:
   - file: docs/MISSION_AND_VALUES
   - file: docs/roadmap
diff --git a/deeplabcut/core/config/base_config.py b/deeplabcut/core/config/base_config.py
index ec89235c3..495511ccc 100644
--- a/deeplabcut/core/config/base_config.py
+++ b/deeplabcut/core/config/base_config.py
@@ -49,7 +49,7 @@ class DLCBaseConfig(BaseModel):
     - Field aliases from `json_schema_extra`.
     """
 
-    model_config = ConfigDict(extra="forbid", validate_assignment=True)
+    model_config = ConfigDict(extra="forbid", validate_assignment=True, arbitrary_types_allowed=True)
 
     # ------------------------------------------------------------------
     # Validation (before pydantic field validation)
@@ -392,10 +392,16 @@ def record_change_note(
         include_caller: bool = False,
         _stack_depth: int = 1,
     ) -> None:
+        field_name = self._resolve_alias(field_name)
+
+        if field_name not in type(self).model_fields:
+            raise KeyError(f"'{type(self).__name__}' has no field '{field_name}'")
+
         if include_caller:
             frame = sys._getframe(_stack_depth)
             filename = frame.f_code.co_filename.rsplit("/", 1)[-1]
             message = f"{message} [{filename}:{frame.f_lineno}]"
+
         self._change_notes[field_name] = message
 
     def log_changes(self) -> None:
diff --git a/deeplabcut/core/weight_init.py b/deeplabcut/core/weight_init.py
index 4f1689b25..19551b9fe 100644
--- a/deeplabcut/core/weight_init.py
+++ b/deeplabcut/core/weight_init.py
@@ -104,8 +104,8 @@ def from_dict(data: dict) -> WeightInitialization:
         if "snapshot_path" not in data:
             return WeightInitialization.from_dict_legacy(data)
 
-        snapshot_path = data['snapshot_path']
-        if data['snapshot_path'] is not None:
+        snapshot_path = data["snapshot_path"]
+        if data["snapshot_path"] is not None:
             snapshot_path = Path(snapshot_path)
 
         detector_snapshot_path = data.get("detector_snapshot_path")
diff --git a/deeplabcut/modelzoo/generalized_data_converter/datasets/materialize.py b/deeplabcut/modelzoo/generalized_data_converter/datasets/materialize.py
index ba4c4ad94..d6ac04d4f 100644
--- a/deeplabcut/modelzoo/generalized_data_converter/datasets/materialize.py
+++ b/deeplabcut/modelzoo/generalized_data_converter/datasets/materialize.py
@@ -612,7 +612,6 @@ def _generic2coco(
                     os.symlink(src, dest)
                 except Exception as err:
                     print(f"Could not create a symlink from {src} to {dest}: {err}")
-                    pass
 
         image["file_name"] = file_name
         lookuptable[dest] = src
diff --git a/deeplabcut/pose_estimation_3d/camera_calibration.py b/deeplabcut/pose_estimation_3d/camera_calibration.py
index 75e6752a6..10ee847ce 100644
--- a/deeplabcut/pose_estimation_3d/camera_calibration.py
+++ b/deeplabcut/pose_estimation_3d/camera_calibration.py
@@ -435,7 +435,7 @@ def check_undistortion(config, cbrow=8, cbcol=6, plot=True):
             f2.suptitle("Undistorted corner points on camera-1 and camera-2", fontsize=25)
             ax1.imshow(cv2.cvtColor(im_remapped1, cv2.COLOR_BGR2RGB))
             ax2.imshow(cv2.cvtColor(im_remapped2, cv2.COLOR_BGR2RGB))
-            for i in range(0, cam1_undistort.shape[1]):
+            for i in range(cam1_undistort.shape[1]):
                 ax1.scatter(
                     [cam1_undistort[-1][i, 0, 0]],
                     [cam1_undistort[-1][i, 0, 1]],
diff --git a/deeplabcut/pose_estimation_pytorch/config/make_pose_config.py b/deeplabcut/pose_estimation_pytorch/config/make_pose_config.py
index 4ac439b25..a60df2d63 100644
--- a/deeplabcut/pose_estimation_pytorch/config/make_pose_config.py
+++ b/deeplabcut/pose_estimation_pytorch/config/make_pose_config.py
@@ -17,6 +17,7 @@
 
 from deeplabcut.core.config import read_config_as_dict, write_config
 from deeplabcut.core.weight_init import WeightInitialization
+from deeplabcut.pose_estimation_pytorch.config.pose import PoseConfig
 from deeplabcut.pose_estimation_pytorch.config.utils import (
     get_config_folder_path,
     load_backbones,
diff --git a/deeplabcut/pose_estimation_pytorch/data/base.py b/deeplabcut/pose_estimation_pytorch/data/base.py
index 51cf65b58..8b6527330 100644
--- a/deeplabcut/pose_estimation_pytorch/data/base.py
+++ b/deeplabcut/pose_estimation_pytorch/data/base.py
@@ -10,16 +10,16 @@
 #
 from __future__ import annotations
 
+import warnings
 from abc import ABC, abstractmethod
 from pathlib import Path
-import warnings
 
 import albumentations as A
 import numpy as np
 
-from deeplabcut.pose_estimation_pytorch.config.pose import PoseConfig
-import deeplabcut.core.config as config_utils
 import deeplabcut.pose_estimation_pytorch.config as config
+from deeplabcut.core.types import DEPRECATED_ARGUMENT
+from deeplabcut.pose_estimation_pytorch.config.pose import PoseConfig
 from deeplabcut.pose_estimation_pytorch.data.dataset import (
     PoseDataset,
     PoseDatasetParameters,
@@ -34,7 +34,6 @@
     map_id_to_annotations,
 )
 from deeplabcut.pose_estimation_pytorch.task import Task
-from deeplabcut.core.types import DEPRECATED_ARGUMENT
 
 
 class Loader(ABC):
@@ -66,18 +65,20 @@ def __init__(
         Args:
             project_root: The root directory of the project.
             image_root: The root directory of the images.
-            model_config (Path | str | PoseConfig | dict): 
+            model_config (Path | str | PoseConfig | dict):
                 The pose model configuration. Can be a path to a YAML file, a PoseConfig object, or a dictionary.
             (model_config_path: The path to the pose model configuration. Deprecated, use `model_config` instead.)
         """
+
         def _resolve_legacy_args(model_config, model_config_path):
             """Support for legacy argument `model_config_path`. returns new model_config arg"""
             if model_config_path:
                 warnings.warn(
                     "argument `model_config_path` in Loader.__init__ is deprecated, use `model_config` instead",
                     DeprecationWarning,
+                    stacklevel=2,
                 )
-                if model_config is not None: 
+                if model_config is not None:
                     raise ValueError(
                         "`model_config_path` and `model_config` arguments cannot be provided together! "
                         "Please provide only `model_config`."
@@ -86,21 +87,24 @@ def _resolve_legacy_args(model_config, model_config_path):
                     model_config_path = Path(model_config_path) / "pytorch_config.yaml"
                 model_config = model_config_path
             elif model_config is None:
-                raise ValueError(
-                    "`model_config` argument must be provided."
-                )
+                raise ValueError("`model_config` argument must be provided.")
             return model_config
-        model_config = _resolve_legacy_args(model_config, model_config_path)    
+
+        model_config = _resolve_legacy_args(model_config, model_config_path)
         self.model_cfg: PoseConfig = PoseConfig.from_any(model_config)
 
         def _infer_model_config_path(model_config: PoseConfig | dict | Path | str) -> Path:
-            """Resolve the pose config path. Either the input is a path, or it is specified in `metadata.pose_config_path` field."""
+            """
+            Resolve the pose config path.
+            Either the input is a path, or it is specified in `metadata.pose_config_path` field.
+            """
             provided_path = Path(model_config) if isinstance(model_config, (Path, str)) else None
             specified_path = self.model_cfg.select("metadata.pose_config_path")
             model_config_path = provided_path or specified_path
             if model_config_path is None:
                 raise ValueError("`model_config` must contain a `metadata.pose_config_path` field.")
             return Path(model_config_path)
+
         self.model_config_path = _infer_model_config_path(model_config)
 
         self.project_root = Path(project_root)
diff --git a/deeplabcut/pose_estimation_pytorch/data/dlcloader.py b/deeplabcut/pose_estimation_pytorch/data/dlcloader.py
index 8aec269f1..2391be7b9 100644
--- a/deeplabcut/pose_estimation_pytorch/data/dlcloader.py
+++ b/deeplabcut/pose_estimation_pytorch/data/dlcloader.py
@@ -20,8 +20,9 @@
 import numpy as np
 import pandas as pd
 import scipy.io as sio
-from deeplabcut.core.config.project_config import ProjectConfig
+
 import deeplabcut.utils.auxiliaryfunctions as af
+from deeplabcut.core.config.project_config import ProjectConfig
 from deeplabcut.core.engine import Engine
 from deeplabcut.generate_training_dataset.trainingsetmanipulation import drop_likelihood_columns
 from deeplabcut.pose_estimation_pytorch.data.base import Loader
@@ -51,9 +52,7 @@ def __init__(
         self._project_config: ProjectConfig = ProjectConfig.from_any(config)
         self._project_root = provided_root_dir or self._project_config.project_path
         if self._project_root is None:
-            raise ValueError(
-                "`config` must contain a `project_path` field."
-            )
+            raise ValueError("`config` must contain a `project_path` field.")
 
         self._shuffle = shuffle
         self._trainset_index = trainset_index
diff --git a/deeplabcut/pose_estimation_pytorch/data/postprocessor.py b/deeplabcut/pose_estimation_pytorch/data/postprocessor.py
index e408b9b49..211d1c751 100644
--- a/deeplabcut/pose_estimation_pytorch/data/postprocessor.py
+++ b/deeplabcut/pose_estimation_pytorch/data/postprocessor.py
@@ -39,7 +39,6 @@ def __call__(self, predictions: Any, context: Context) -> Any:
         Returns:
             a single post-processed prediction
         """
-        pass
 
 
 def build_bottom_up_postprocessor(
diff --git a/deeplabcut/pose_estimation_pytorch/data/preprocessor.py b/deeplabcut/pose_estimation_pytorch/data/preprocessor.py
index 762cd632e..024f0260d 100644
--- a/deeplabcut/pose_estimation_pytorch/data/preprocessor.py
+++ b/deeplabcut/pose_estimation_pytorch/data/preprocessor.py
@@ -51,7 +51,6 @@ def __call__(self, image: Image, context: Context) -> tuple[Image, Context]:
         Returns:
             the pre-processed image (or batch of images) and their context
         """
-        pass
 
 
 def build_bottom_up_preprocessor(color_mode: str, transform: A.BaseCompose) -> Preprocessor:
diff --git a/deeplabcut/pose_estimation_pytorch/models/backbones/base.py b/deeplabcut/pose_estimation_pytorch/models/backbones/base.py
index dedac0f70..9f5a3972a 100644
--- a/deeplabcut/pose_estimation_pytorch/models/backbones/base.py
+++ b/deeplabcut/pose_estimation_pytorch/models/backbones/base.py
@@ -54,7 +54,6 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         Returns:
             a feature map for the input, of shape (batch_size, c', h', w')
         """
-        pass
 
     def freeze_batch_norm_layers(self) -> None:
         """Freezes batch norm layers.
diff --git a/deeplabcut/pose_estimation_pytorch/models/detectors/base.py b/deeplabcut/pose_estimation_pytorch/models/detectors/base.py
index 09bcf4e9f..689702e41 100644
--- a/deeplabcut/pose_estimation_pytorch/models/detectors/base.py
+++ b/deeplabcut/pose_estimation_pytorch/models/detectors/base.py
@@ -85,7 +85,6 @@ def forward(
             losses: {'loss_name': loss_value}
             detections: for each of the b images, {"boxes": bounding_boxes}
         """
-        pass
 
     @abstractmethod
     def get_target(self, labels: dict) -> list[dict]:
@@ -97,7 +96,6 @@ def get_target(self, labels: dict) -> list[dict]:
         Returns:
             list of dictionaries, each representing target information for a single annotation.
         """
-        pass
 
     def freeze_batch_norm_layers(self) -> None:
         """Freezes batch norm layers.
diff --git a/deeplabcut/pose_estimation_pytorch/models/heads/base.py b/deeplabcut/pose_estimation_pytorch/models/heads/base.py
index bb459f72d..c4be4f5bc 100644
--- a/deeplabcut/pose_estimation_pytorch/models/heads/base.py
+++ b/deeplabcut/pose_estimation_pytorch/models/heads/base.py
@@ -97,7 +97,6 @@ def forward(self, x: torch.Tensor) -> dict[str, torch.Tensor]:
         Returns:
             the head outputs (e.g. "heatmap", "locref")
         """
-        pass
 
     def get_loss(
         self,
@@ -172,4 +171,3 @@ def convert_weights(
                 )
                 ``
         """
-        pass
diff --git a/deeplabcut/pose_estimation_pytorch/models/modules/conv_block.py b/deeplabcut/pose_estimation_pytorch/models/modules/conv_block.py
index 529db6495..8c1e00ee6 100644
--- a/deeplabcut/pose_estimation_pytorch/models/modules/conv_block.py
+++ b/deeplabcut/pose_estimation_pytorch/models/modules/conv_block.py
@@ -50,7 +50,6 @@ def forward(self, x: torch.Tensor):
         Returns:
             Output tensor.
         """
-        pass
 
     def _init_weights(self, pretrained: str | None):
         """Method for initializing block weights from pretrained models.
diff --git a/deeplabcut/pose_estimation_pytorch/models/necks/base.py b/deeplabcut/pose_estimation_pytorch/models/necks/base.py
index 336b1a9ef..737005e61 100644
--- a/deeplabcut/pose_estimation_pytorch/models/necks/base.py
+++ b/deeplabcut/pose_estimation_pytorch/models/necks/base.py
@@ -33,7 +33,6 @@ def forward(self, x: torch.Tensor):
         Returns:
             Output tensor.
         """
-        pass
 
     def _init_weights(self, pretrained: str):
         """Initialize the Neck with pretrained weights.
diff --git a/deeplabcut/pose_estimation_pytorch/models/predictors/base.py b/deeplabcut/pose_estimation_pytorch/models/predictors/base.py
index 42c9fb586..c175d7162 100644
--- a/deeplabcut/pose_estimation_pytorch/models/predictors/base.py
+++ b/deeplabcut/pose_estimation_pytorch/models/predictors/base.py
@@ -61,4 +61,3 @@ def forward(self, stride: float, outputs: dict[str, torch.Tensor]) -> dict[str,
         Raises:
             NotImplementedError: This method must be implemented in subclasses.
         """
-        pass
diff --git a/deeplabcut/pose_estimation_pytorch/runners/inference.py b/deeplabcut/pose_estimation_pytorch/runners/inference.py
index 2a062be15..7f60db8d4 100644
--- a/deeplabcut/pose_estimation_pytorch/runners/inference.py
+++ b/deeplabcut/pose_estimation_pytorch/runners/inference.py
@@ -256,7 +256,7 @@ def __init__(
             self.inference_cfg = InferenceConfig.from_dict(inference_cfg)
         elif inference_cfg is None:
             self.inference_cfg = InferenceConfig()
-        else: 
+        else:
             raise ValueError(f"Invalid inference config: {inference_cfg}")
 
         if self.snapshot_path is not None and self.snapshot_path != "":
@@ -539,7 +539,11 @@ def _safe_get(self) -> Any:
                 return item
             except Empty:
                 # check if producer is still running
-                if self._stop_event.is_set() or self._preprocessing_thread is None or not self._preprocessing_thread.is_alive():
+                if (
+                    self._stop_event.is_set()
+                    or self._preprocessing_thread is None
+                    or not self._preprocessing_thread.is_alive()
+                ):
                     return None
                 continue
 
diff --git a/deeplabcut/pose_estimation_pytorch/runners/logger.py b/deeplabcut/pose_estimation_pytorch/runners/logger.py
index 58250ae4b..215e36762 100644
--- a/deeplabcut/pose_estimation_pytorch/runners/logger.py
+++ b/deeplabcut/pose_estimation_pytorch/runners/logger.py
@@ -152,7 +152,6 @@ def log_images(
             targets: the targets for each model head
             step: the current step
         """
-        pass
 
     def select_images_to_log(self, train: DataLoader, valid: DataLoader) -> None:
         """Selects the train and test images to log.
@@ -448,7 +447,6 @@ def log_config(self, config: dict = None) -> None:
         Args:
             config: Experiment config file.
         """
-        pass
 
     def _load_existing_data(self) -> None:
         """Loads existing CSV data if the log file exists."""
diff --git a/deeplabcut/pose_estimation_pytorch/runners/schedulers.py b/deeplabcut/pose_estimation_pytorch/runners/schedulers.py
index 381a1a939..a677b95c0 100644
--- a/deeplabcut/pose_estimation_pytorch/runners/schedulers.py
+++ b/deeplabcut/pose_estimation_pytorch/runners/schedulers.py
@@ -13,7 +13,6 @@
 from typing import Any
 
 import torch
-from omegaconf import DictConfig, ListConfig, OmegaConf
 from torch.optim.lr_scheduler import _LRScheduler
 
 
@@ -87,9 +86,6 @@ def build_scheduler(
         # unpicklable omegaconf objects with _parent references.
         # TODO @deruyter92: decide on typed / plain list / dict in upstream code
         # Then this check can be removed.
-        if isinstance(param, (ListConfig, DictConfig)):
-            param = OmegaConf.to_container(param, resolve=True)
-
         if isinstance(param, list):
             param = [_parse_scheduler_param(p, optimizer) for p in param]
         else:
diff --git a/deeplabcut/pose_estimation_tensorflow/nnets/utils.py b/deeplabcut/pose_estimation_tensorflow/nnets/utils.py
index 1087fef57..5641ffc6c 100644
--- a/deeplabcut/pose_estimation_tensorflow/nnets/utils.py
+++ b/deeplabcut/pose_estimation_tensorflow/nnets/utils.py
@@ -213,5 +213,3 @@ def drop_connect(inputs, is_training, drop_connect_rate):
 
 class DepthwiseConv2D(tf.keras.layers.DepthwiseConv2D, tf.compat.v1.layers.Layer):
     """Wrap keras DepthwiseConv2D to tf.layers."""
-
-    pass
diff --git a/deeplabcut/refine_training_dataset/outlier_frames.py b/deeplabcut/refine_training_dataset/outlier_frames.py
index 7772df983..2a60ed37e 100644
--- a/deeplabcut/refine_training_dataset/outlier_frames.py
+++ b/deeplabcut/refine_training_dataset/outlier_frames.py
@@ -1122,7 +1122,7 @@ def merge_datasets(config, forceiterate=None):
         else:
             print("The following folder was not manually refined,...", folder)
             flagged = True
-            pass  # this folder does not contain a MachineLabelsRefine file (not updated...)
+            # this folder does not contain a MachineLabelsRefine file (not updated...)
 
     if not flagged:
         # updates iteration by 1
diff --git a/deeplabcut/refine_training_dataset/tracklets.py b/deeplabcut/refine_training_dataset/tracklets.py
index c89e24efb..408a2449b 100644
--- a/deeplabcut/refine_training_dataset/tracklets.py
+++ b/deeplabcut/refine_training_dataset/tracklets.py
@@ -193,7 +193,7 @@ def get_frame_ind(s):
 
             # Map a tracklet # to the animal ID it belongs to or the bodypart # it corresponds to.
             self.individuals = self.cfg["individuals"] + (["single"] if len(self.cfg["uniquebodyparts"]) else [])
-            self.tracklet2id = [i for i in range(0, self.nindividuals) for _ in bodyparts_multi] + [
+            self.tracklet2id = [i for i in range(self.nindividuals) for _ in bodyparts_multi] + [
                 self.nindividuals
             ] * len(bodyparts_single)
             bps = bodyparts_multi + bodyparts_single
diff --git a/deeplabcut/utils/auxiliaryfunctions.py b/deeplabcut/utils/auxiliaryfunctions.py
index c230104a2..d9edc8ee3 100644
--- a/deeplabcut/utils/auxiliaryfunctions.py
+++ b/deeplabcut/utils/auxiliaryfunctions.py
@@ -22,7 +22,6 @@
 
 import os
 import pickle
-import warnings
 from collections.abc import Sequence
 from pathlib import Path
 
@@ -48,9 +47,7 @@ def read_plainconfig(configname: str | Path) -> dict:
     return core_config.read_config_as_dict(config_path=configname)
 
 
-def write_plainconfig(
-    configname: str | Path, cfg: dict, overwrite: bool = True
-) -> None:
+def write_plainconfig(configname: str | Path, cfg: dict, overwrite: bool = True) -> None:
     """Write a config dict to YAML (alias for write_config). See deeplabcut.core.config."""
     core_config.write_config(config_path=configname, config=cfg, overwrite=overwrite)
 
@@ -612,7 +609,7 @@ def find_analyzed_data(folder, videoname: str, scorer: str, filtered=False, trac
     candidates = []
     for file in grab_files_in_folder(folder, "h5"):
         stem = Path(file).stem.replace("_filtered", "")
-        starts_by_scorer = file.startswith(videoname + scorer) or file.startswith(videoname + scorer_legacy)
+        starts_by_scorer = file.startswith((videoname + scorer, videoname + scorer_legacy))
         if tracker:
             matches_tracker = stem.endswith(tracker)
         else:
diff --git a/deeplabcut/utils/auxiliaryfunctions_3d.py b/deeplabcut/utils/auxiliaryfunctions_3d.py
index 5d2c22300..58fdd7eee 100644
--- a/deeplabcut/utils/auxiliaryfunctions_3d.py
+++ b/deeplabcut/utils/auxiliaryfunctions_3d.py
@@ -66,7 +66,7 @@ def create_empty_df(dataframe, scorer, flag):
                 [[scorer], [bodypart], ["x", "y", "z"]],
                 names=["scorer", "bodyparts", "coords"],
             )
-        frame = pd.DataFrame(a, columns=pdindex, index=range(0, df.shape[0]))
+        frame = pd.DataFrame(a, columns=pdindex, index=range(df.shape[0]))
         dataFrame = pd.concat([frame, dataFrame], axis=1)
     return (dataFrame, scorer, bodyparts)
 
diff --git a/deeplabcut/utils/make_labeled_video.py b/deeplabcut/utils/make_labeled_video.py
index 959050908..3a786d4e1 100644
--- a/deeplabcut/utils/make_labeled_video.py
+++ b/deeplabcut/utils/make_labeled_video.py
@@ -1348,12 +1348,10 @@ def create_video_with_all_detections(
                         )
                 except ValueError as err:  # No data stored for that particular frame
                     print(n, f"no data: {err}")
-                    pass
                 try:
                     clip.save_frame(frame)
                 except Exception:
                     print(n, "frame writing error.")
-                    pass
             clip.close()
         else:
             print("Detections already plotted, ", outputname)
diff --git a/deeplabcut/utils/skeleton.py b/deeplabcut/utils/skeleton.py
index 9b0ccd823..1ad1729af 100644
--- a/deeplabcut/utils/skeleton.py
+++ b/deeplabcut/utils/skeleton.py
@@ -27,14 +27,12 @@
 import pandas as pd
 from matplotlib.collections import LineCollection
 from matplotlib.widgets import Button, LassoSelector
-from ruamel.yaml import YAML
 from scipy.spatial import KDTree
 from skimage import io
 
+from deeplabcut.core.config import read_config, read_config_as_dict, write_config
 from deeplabcut.generate_training_dataset.trainingsetmanipulation import drop_likelihood_columns
 
-from deeplabcut.core.config import read_config_as_dict, write_config
-
 
 class SkeletonBuilder:
     def __init__(self, config_path):
diff --git a/deeplabcut/utils/video_processor.py b/deeplabcut/utils/video_processor.py
index 5ce7bee90..01c5ff39e 100644
--- a/deeplabcut/utils/video_processor.py
+++ b/deeplabcut/utils/video_processor.py
@@ -86,27 +86,21 @@ def frame_count(self):
 
     def get_video(self):
         """Implement your own."""
-        pass
 
     def get_info(self):
         """Implement your own."""
-        pass
 
     def create_video(self):
         """Implement your own."""
-        pass
 
     def _read_frame(self):
         """Implement your own."""
-        pass
 
     def save_frame(self, frame):
         """Implement your own."""
-        pass
 
     def close(self):
         """Implement your own."""
-        pass
 
 
 class VideoProcessorCV(VideoProcessor):
diff --git a/tests/core/config/test_config_breakage.py b/tests/core/config/test_config_breakage.py
new file mode 100644
index 000000000..81cba7204
--- /dev/null
+++ b/tests/core/config/test_config_breakage.py
@@ -0,0 +1,236 @@
+"""Pathological casess tests for the centralized config model."""
+
+from __future__ import annotations
+
+import enum
+import logging
+from pathlib import Path
+
+import pytest
+from pydantic import Field, ValidationError
+
+from deeplabcut.core.config import DLCBaseConfig, DLCVersionedConfig, ProjectConfig
+from deeplabcut.utils.deprecation import DLCDeprecationWarning
+
+# -----------------------------------------------------------------------------
+# In-place nested mutation
+# -----------------------------------------------------------------------------
+
+
+@pytest.mark.xfail(
+    reason="This may make validation difficult and cause subtle issues "
+    "if we start changing how functions such as append() operate."
+    " Avoiding lists in configs could be a better long-term solution."
+)
+def test_in_place_list_mutation_should_be_validated_like_assignment():
+    """Appending to a config list changes config state without calling __setattr__.
+
+    A strict config system should reject invalid values even when users mutate an
+    existing list instead of assigning a replacement list.
+
+    Note that this pattern would incur validation:
+        cfg.TrainingFraction = [*cfg.TrainingFraction, 0.8]
+        cfg.video_sets = {**cfg.video_sets, "video.mp4": {"crop": "0, 100, 0, 100"}}
+    """
+    cfg = ProjectConfig(TrainingFraction=[0.95])
+    cfg.mark_clean()
+
+    with pytest.raises(ValidationError):
+        cfg.TrainingFraction.append(1.5)
+
+
+@pytest.mark.xfail(reason="Not implemented yet")
+@pytest.mark.parametrize(
+    ("field_name", "mutate"),
+    [
+        ("TrainingFraction", lambda cfg: cfg.TrainingFraction.append(0.8)),
+        ("video_sets", lambda cfg: cfg.video_sets.__setitem__("video.mp4", {"crop": "0, 100, 0, 100"})),
+    ],
+)
+def test_in_place_nested_mutation_should_mark_config_dirty(field_name, mutate):
+    """Mutating nested containers changes the saved config but may bypass dirty tracking.
+
+    Dirty tracking is useful only if semantic config changes are recorded, whether
+    the user assigns a whole field or mutates a nested list/dict in place.
+    This ties with the problem that some nested fields are still dict instead of config models,
+    maybe once we have full config models we can come up with a parent/child system where changes in a child
+    are detected when querying the parent for its dirty state (or a simpler equivalent)
+    """
+    cfg = ProjectConfig(TrainingFraction=[0.95], video_sets={})
+    cfg.mark_clean()
+
+    mutate(cfg)
+
+    assert cfg.is_dirty
+    assert field_name in cfg.dirty_fields
+
+
+# -----------------------------------------------------------------------------
+# Private state isolation
+# -----------------------------------------------------------------------------
+
+
+class _TrackedForIsolation(DLCVersionedConfig):
+    name: str = "default"
+    count: int = 0
+
+
+def test_dirty_state_is_not_shared_between_instances():
+    """Private dirty-field state must be per-instance, not shared via a mutable default."""
+    first = _TrackedForIsolation()
+    second = _TrackedForIsolation()
+
+    first.name = "changed"
+
+    assert "name" in first.dirty_fields
+    assert not second.is_dirty
+    assert "name" not in second.dirty_fields
+
+
+def test_change_notes_are_not_shared_between_instances():
+    """Private change-note state must be per-instance, not shared via a mutable default."""
+    first = _TrackedForIsolation()
+    second = _TrackedForIsolation()
+
+    first.record_change_note("name", "name changed on first instance")
+
+    assert first.change_notes == ["name changed on first instance"]
+    assert second.change_notes == []
+
+
+# -----------------------------------------------------------------------------
+# Changes note consistency
+# -----------------------------------------------------------------------------
+
+
+def test_change_note_should_reject_unknown_field_names():
+    """Notes for misspelled fields are silently lost during logging.
+
+    Rejecting unknown field names catches typos at the call site instead of
+    storing a note that can never match a dirty field.
+    """
+    cfg = ProjectConfig()
+
+    with pytest.raises(KeyError):
+        cfg.record_change_note("not_a_real_field", "this note should not be accepted")
+
+
+def test_change_note_recorded_with_alias_should_follow_canonical_dirty_field(caplog):
+    """Aliases should not split notes from the canonical dirty field name.
+
+    If a note is recorded with a deprecated alias, log_changes should still use
+    that note when the canonical field is modified.
+    """
+    cfg = ProjectConfig()
+    cfg.mark_clean()
+
+    cfg.record_change_note("with_identity", "identity changed through compatibility alias")
+    cfg.identity = True
+
+    with caplog.at_level(logging.INFO):
+        cfg.log_changes()
+
+    assert "identity changed through compatibility alias" in caplog.text
+
+
+# -----------------------------------------------------------------------------
+# Nested YAML comments and nested serialization
+# -----------------------------------------------------------------------------
+
+
+class _CommentedInner(DLCBaseConfig):
+    threshold: float = Field(
+        default=0.5,
+        json_schema_extra={"comment": "Nested threshold comment"},
+    )
+
+
+class _CommentedOuter(DLCBaseConfig):
+    inner: _CommentedInner = Field(
+        default_factory=_CommentedInner,
+        json_schema_extra={"comment": "Inner config section"},
+    )
+
+
+@pytest.mark.xfail(reason="YAML comments are currently applied only to top-level config fields.")
+def test_to_yaml_should_emit_comments_for_nested_config_fields(tmp_path):
+    """Nested config models can have field comments too.
+
+    This protects against only applying YAML comments to the top-level model and
+    silently dropping useful nested schema documentation.
+    """
+    path = tmp_path / "commented.yaml"
+
+    _CommentedOuter().to_yaml(path)
+
+    text = path.read_text()
+    assert "Inner config section" in text
+    assert "Nested threshold comment" in text
+
+
+class _SerializationMode(enum.Enum):
+    FAST = "fast"
+    ACCURATE = "accurate"
+
+
+class _SerializableInner(DLCBaseConfig):
+    output_path: Path = Path("outputs/predictions")
+    mode: _SerializationMode = _SerializationMode.FAST
+
+
+class _SerializableOuter(DLCBaseConfig):
+    inner: _SerializableInner = Field(default_factory=_SerializableInner)
+
+
+def test_nested_config_normalization_should_handle_paths_and_enums():
+    """Nested model dumps should normalize non-primitive values before YAML output.
+
+    Path and Enum values are common in configs and should become plain YAML-safe
+    values even when they appear inside nested config models.
+    """
+    cfg = _SerializableOuter()
+
+    assert cfg.to_dict(normalize=True) == {
+        "inner": {
+            "output_path": str(Path("outputs/predictions")),
+            "mode": "fast",
+        }
+    }
+
+
+def test_nested_config_to_yaml_should_write_plain_path_and_enum_values(tmp_path):
+    """YAML output should not rely on Python-specific object representation.
+
+    This catches failures where nested BaseModel values, pathlib paths, or enums
+    are passed to the YAML dumper without normalization.
+    """
+    path = tmp_path / "serializable.yaml"
+
+    _SerializableOuter().to_yaml(path)
+
+    text = path.read_text()
+    assert str(Path("outputs/predictions")) in text
+    assert "fast" in text
+
+
+# -----------------------------------------------------------------------------
+# Alias warnings and canonical writes
+# -----------------------------------------------------------------------------
+
+
+def test_item_assignment_with_alias_should_warn_once_and_track_canonical_field():
+    """Dict-style alias assignment should not warn twice or track the alias name.
+
+    __setitem__ resolves aliases before delegating to assignment validation, so
+    the visible side effects should be one warning and one canonical dirty field.
+    """
+    cfg = ProjectConfig()
+    cfg.mark_clean()
+
+    with pytest.warns(DLCDeprecationWarning, match="with_identity") as caught:
+        cfg["with_identity"] = True
+
+    assert len(caught) == 1
+    assert cfg.identity is True
+    assert "identity" in cfg.dirty_fields
+    assert "with_identity" not in cfg.dirty_fields
diff --git a/tests/pose_estimation_pytorch/runners/test_schedulers.py b/tests/pose_estimation_pytorch/runners/test_schedulers.py
index 37e98ea6a..af745016a 100644
--- a/tests/pose_estimation_pytorch/runners/test_schedulers.py
+++ b/tests/pose_estimation_pytorch/runners/test_schedulers.py
@@ -39,7 +39,7 @@ def generate_random_lr_list(num_floats: int):
 
 @pytest.mark.parametrize(
     "milestones, lr_list",
-    [([10, 430], [[0.05], [0.005]]), (list(sorted(random.sample(range(0, 999), 2))), generate_random_lr_list(2))],
+    [([10, 430], [[0.05], [0.005]]), (list(sorted(random.sample(range(999), 2))), generate_random_lr_list(2))],
 )
 def test_scheduler(milestones, lr_list):
     """Testing schedulers.py.