Reformat Markdown code blocks (#12795)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/modes/track.md

@@ -70,14 +70,14 @@ To run the tracker on video streams, use a trained Detect, Segment or Pose model
         from ultralytics import YOLO
 
         # Load an official or custom model
-        model = YOLO('yolov8n.pt')  # Load an official Detect model
-        model = YOLO('yolov8n-seg.pt')  # Load an official Segment model
-        model = YOLO('yolov8n-pose.pt')  # Load an official Pose model
-        model = YOLO('path/to/best.pt')  # Load a custom trained model
+        model = YOLO("yolov8n.pt")  # Load an official Detect model
+        model = YOLO("yolov8n-seg.pt")  # Load an official Segment model
+        model = YOLO("yolov8n-pose.pt")  # Load an official Pose model
+        model = YOLO("path/to/best.pt")  # Load a custom trained model
 
         # Perform tracking with the model
-        results = model.track(source="https://youtu.be/LNwODJXcvt4", show=True)  # Tracking with default tracker
-        results = model.track(source="https://youtu.be/LNwODJXcvt4", show=True, tracker="bytetrack.yaml")  # Tracking with ByteTrack tracker
+        results = model.track("https://youtu.be/LNwODJXcvt4", show=True)  # Tracking with default tracker
+        results = model.track("https://youtu.be/LNwODJXcvt4", show=True, tracker="bytetrack.yaml")  # with ByteTrack
         ```
 
     === "CLI"
@@ -113,7 +113,7 @@ Tracking configuration shares properties with Predict mode, such as `conf`, `iou
         from ultralytics import YOLO
 
         # Configure the tracking parameters and run the tracker
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
         results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True)
         ```
 
@@ -136,8 +136,8 @@ Ultralytics also allows you to use a modified tracker configuration file. To do
         from ultralytics import YOLO
 
         # Load the model and run the tracker with a custom configuration file
-        model = YOLO('yolov8n.pt')
-        results = model.track(source="https://youtu.be/LNwODJXcvt4", tracker='custom_tracker.yaml')
+        model = YOLO("yolov8n.pt")
+        results = model.track(source="https://youtu.be/LNwODJXcvt4", tracker="custom_tracker.yaml")
         ```
 
     === "CLI"
@@ -162,7 +162,7 @@ Here is a Python script using OpenCV (`cv2`) and YOLOv8 to run object tracking o
     from ultralytics import YOLO
 
     # Load the YOLOv8 model
-    model = YOLO('yolov8n.pt')
+    model = YOLO("yolov8n.pt")
 
     # Open the video file
     video_path = "path/to/video.mp4"
@@ -210,11 +210,10 @@ In the following example, we demonstrate how to utilize YOLOv8's tracking capabi
 
     import cv2
     import numpy as np
-
     from ultralytics import YOLO
 
     # Load the YOLOv8 model
-    model = YOLO('yolov8n.pt')
+    model = YOLO("yolov8n.pt")
 
     # Open the video file
     video_path = "path/to/video.mp4"
@@ -284,6 +283,7 @@ Finally, after all threads have completed their task, the windows displaying the
 
     ```python
     import threading
+
     import cv2
     from ultralytics import YOLO
 
@@ -318,7 +318,7 @@ Finally, after all threads have completed their task, the windows displaying the
             cv2.imshow(f"Tracking_Stream_{file_index}", res_plotted)
 
             key = cv2.waitKey(1)
-            if key == ord('q'):
+            if key == ord("q"):
                 break
 
         # Release video sources
@@ -326,8 +326,8 @@ Finally, after all threads have completed their task, the windows displaying the
 
 
     # Load the models
-    model1 = YOLO('yolov8n.pt')
-    model2 = YOLO('yolov8n-seg.pt')
+    model1 = YOLO("yolov8n.pt")
+    model2 = YOLO("yolov8n-seg.pt")
 
     # Define the video files for the trackers
     video_file1 = "path/to/video1.mp4"  # Path to video file, 0 for webcam