…onverter_config.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

- reading breaks for corrupted yaml
- added tests for prefered behavior that is currently not implemented (yaml safeloading and validating config keys)

Part 1. of migration to typed configs, see issue #3193

This mixin provides methods for:
    - Loading configurations from dictionaries or YAML files
    - Validating configuration data against pydantic models
    - Converting configurations to dictionaries
    - Pretty printing configuration data

add imports from utils

- Introduced new configuration classes for inference, logging, model, pose, project, runner, and training settings.
- Refactored data loading mechanisms to utilize new configuration structures.
- Moved the multithreading and compilation options in inference configuration to the config module.
- Typed configuration for logging.
- Updated dataset loaders to accept model configurations directly or via file paths.

(The fields are kept identical to old multianimal project configs for now)

move ProjectConfig to deeplabcut/core/config

…correct TypeAdapter usage)

The return value should be the dictionary, not the instantiated transforms

Part 2. of migration to typed configs, see issue #3193

…nfig

…reading from yaml.

The head_cfg (a part of PoseConfig) was used for dumping modules. This is not supported for typed configs.

The container is now replaced with a plain dict type instead of config.

Part 3. of migration to typed configs, see issue #3193

…m serialization