ultralytics 8.0.91 tracker fix and docs comments (#2343)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>

docs/usage/callbacks.md

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 ## Callbacks
 
 Ultralytics framework supports callbacks as entry points in strategic stages of train, val, export, and predict modes.
@@ -13,7 +17,7 @@ In this example, we want to return the original frame with each result object. H
 ```python
 def on_predict_batch_end(predictor):
     # Retrieve the batch data
-    _, _, im0s, _, _ = predictor.batch
+    _, im0s, _, _ = predictor.batch
     
     # Ensure that im0s is a list
     im0s = im0s if isinstance(im0s, list) else [im0s]

docs/usage/cfg.md

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 YOLO settings and hyperparameters play a critical role in the model's performance, speed, and accuracy. These settings
 and hyperparameters can affect the model's behavior at various stages of the model development process, including
 training, validation, and prediction.

docs/usage/cli.md

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 # Command Line Interface Usage
 
 The YOLO command line interface (CLI) allows for simple single-line commands without the need for a Python environment.

docs/usage/engine.md

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 Both the Ultralytics YOLO command-line and python interfaces are simply a high-level abstraction on the base engine
 executors. Let's take a look at the Trainer engine.
 

docs/usage/hyperparameter_tuning.md

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 # Hyperparameter Tuning with Ray Tune and YOLOv8
 
 Hyperparameter tuning (or hyperparameter optimization) is the process of determining the right combination of hyperparameters that maximizes model performance. It works by running multiple trials in a single training process, evaluating the performance of each trial, and selecting the best hyperparameter values based on the evaluation results.

docs/usage/python.md

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 # Python Usage
 
 Welcome to the YOLOv8 Python Usage documentation! This guide is designed to help you seamlessly integrate YOLOv8 into