Add docformatter to pre-commit (#5279)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>

ultralytics/utils/callbacks/dvc.py

@@ -25,6 +25,7 @@ except (ImportError, AssertionError, TypeError):
 
 
 def _log_images(path, prefix=''):
+    """Logs images at specified path with an optional prefix using DVCLive."""
     if live:
         name = path.name
 
@@ -38,6 +39,7 @@ def _log_images(path, prefix=''):
 
 
 def _log_plots(plots, prefix=''):
+    """Logs plot images for training progress if they have not been previously processed."""
     for name, params in plots.items():
         timestamp = params['timestamp']
         if _processed_plots.get(name) != timestamp:
@@ -46,6 +48,7 @@ def _log_plots(plots, prefix=''):
 
 
 def _log_confusion_matrix(validator):
+    """Logs the confusion matrix for the given validator using DVCLive."""
     targets = []
     preds = []
     matrix = validator.confusion_matrix.matrix
@@ -62,6 +65,7 @@ def _log_confusion_matrix(validator):
 
 
 def on_pretrain_routine_start(trainer):
+    """Initializes DVCLive logger for training metadata during pre-training routine."""
     try:
         global live
         live = dvclive.Live(save_dvc_exp=True, cache_images=True)
@@ -71,20 +75,24 @@ def on_pretrain_routine_start(trainer):
 
 
 def on_pretrain_routine_end(trainer):
+    """Logs plots related to the training process at the end of the pretraining routine."""
     _log_plots(trainer.plots, 'train')
 
 
 def on_train_start(trainer):
+    """Logs the training parameters if DVCLive logging is active."""
     if live:
         live.log_params(trainer.args)
 
 
 def on_train_epoch_start(trainer):
+    """Sets the global variable _training_epoch value to True at the start of training each epoch."""
     global _training_epoch
     _training_epoch = True
 
 
 def on_fit_epoch_end(trainer):
+    """Logs training metrics and model info, and advances to next step on the end of each fit epoch."""
     global _training_epoch
     if live and _training_epoch:
         all_metrics = {**trainer.label_loss_items(trainer.tloss, prefix='train'), **trainer.metrics, **trainer.lr}
@@ -104,6 +112,7 @@ def on_fit_epoch_end(trainer):
 
 
 def on_train_end(trainer):
+    """Logs the best metrics, plots, and confusion matrix at the end of training if DVCLive is active."""
     if live:
         # At the end log the best metrics. It runs validator on the best model internally.
         all_metrics = {**trainer.label_loss_items(trainer.tloss, prefix='train'), **trainer.metrics, **trainer.lr}