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d947b7beb3
- Expanded module-level and function/class docstrings in optimizer.py, scheduler.py, and trainer.py - Described mathematical foundations, theoretical motivations, and provided detailed usage examples for students - All docstrings in Russian, clear scientific style test(training): add comprehensive tests for optimizer, scheduler, and trainer modules - Added new test files for get_optimizer, get_linear_schedule_with_warmup, and Trainer - Tests cover parameter handling, edge cases, and expected learning dynamics (lr schedules and loss behavior) - Trainer now logs average epoch losses to self.loss_history for testability and analysis refactor(training/trainer): log epoch loss to loss_history for downstream analysis and tests BREAKING CHANGE: Trainer.loss_history is a new attribute consolidating average losses per epoch, enabling robust learning dynamics assertions in tests