Visualization fixes, clean-up and coverage improvements - #3447

Adds a new test module for `deeplabcut.utils.visualization` focused on evaluation plotting behavior. The tests cover single-animal label handling across scorers, coordinate arrangement for both plotting modes, mismatch/malformed-data skip paths with expected messages, invalid mode validation, output filename conventions in `save_labeled_frame`, and bounding-box linestyle behavior in `make_multianimal_labeled_image`.

Tightened `plot_evaluation_results` by constraining `mode` with a `Literal` type and adding an explicit runtime `ValueError` for unsupported values. The plotting branch logic now treats non-`bodypart` mode consistently as `individual`, and warning messages for individual-count/reshape issues were rewritten to be clearer and more specific. Tests were updated to match the new warning text, and a matplotlib cleanup fixture was added to close figures after each test.

Use a row-scoped `plot_unique_for_row` flag in `plot_evaluation_results` so a reshape failure only disables unique-bodypart plotting for the current image. This prevents one bad row from mutating `plot_unique_bodyparts` and incorrectly skipping unique overlays for all subsequent rows.

Improve `visualization.py` with clearer type aliases, small cleanup of unused variables, and better API messaging (including a specific `ValueError` for missing `individuals`). In evaluation plotting, tighten consistency checks by comparing ordered bodypart/individual labels, skip mismatched rows with explicit warnings, simplify auto bounding-box color handling, and wrap plotting in `try/finally` to always close figures and avoid leaked matplotlib resources.

Removes the duplicated legacy license/docstring block from `deeplabcut/utils/visualization.py` and adds the standard DeepLabCut license header comments to `tests/utils/test_visualization.py` for consistent file metadata across the codebase.

Refactors unique-bodypart extraction in `plot_evaluation_results` to index the `single` individual directly and validate bodypart alignment between ground truth and predictions before reshaping. This avoids reshape failures from mismatched bodyparts and adds a clearer warning path when unique data is inconsistent.

This updates `visualization.py` to use consistent frame-dimension extraction (`shape[:2]`) across image plotting paths, making handling cleaner for both grayscale and color frames. It also renames `BoundingBoxesColor` to `BoundingBoxColor` for clearer typing and fixes a typo in the individuals-coloring error message.

Removes the evaluation-plot guard that skipped images when multi-individual ground-truth and prediction labels differed in order/content. This keeps visualization running for those cases while retaining existing count and bodypart mismatch checks.

Update evaluation plotting to skip unique-bodypart rendering when the "single" subset is empty before indexing scorer/model columns. This avoids downstream mismatch/reshape errors and keeps the existing warning-based fallback behavior for mismatched bodyparts or invalid shapes.

Update `plot_evaluation_results` to use the individual count as the colormap offset in non-bodypart mode, instead of always using `0`. This prevents color index overlap and keeps prediction colors aligned with the selected plotting mode. Also document the `color_offset` parameter in `make_multianimal_labeled_image` for clarity.

Extend `evaluation_dataframe_factory` to generate GT/pred columns for unique bodyparts and expose them in metadata. Add a new parametrized test that verifies `plot_evaluation_results` arranges unique bodyparts correctly in both `bodypart` and `individual` modes, including expected coordinate/probability shapes and color offset handling.