Docs Ruff codeblocks reformat and fix (#12847)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/integrations/tensorboard.md

@@ -39,15 +39,15 @@ TensorBoard is conveniently pre-installed with YOLOv8, eliminating the need for
 
 For detailed instructions and best practices related to the installation process, be sure to check our [YOLOv8 Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
 
-## Configuring TensorBoard for Google Collab
+## Configuring TensorBoard for Google Colab
 
 When using Google Colab, it's important to set up TensorBoard before starting your training code:
 
-!!! Example "Configure TensorBoard for Google Collab"
+!!! Example "Configure TensorBoard for Google Colab"
 
     === "Python"
 
-        ```python
+        ```ipython
         %load_ext tensorboard
         %tensorboard --logdir path/to/runs
         ```

docs/en/integrations/tensorrt.md

@@ -153,15 +153,16 @@ Experimentation by NVIDIA led them to recommend using at least 500 calibration i
         model = YOLO("yolov8n.pt")
         model.export(
             format="engine",
-            dynamic=True, #(1)!
-            batch=8, #(2)!
-            workspace=4, #(3)!
+            dynamic=True,  # (1)!
+            batch=8,  # (2)!
+            workspace=4,  # (3)!
             int8=True,
-            data="coco.yaml", #(4)!
+            data="coco.yaml",  # (4)!
         )
 
         # Load the exported TensorRT INT8 model
         model = YOLO("yolov8n.engine", task="detect")
+
         # Run inference
         result = model.predict("https://ultralytics.com/images/bus.jpg")
         ```
@@ -385,36 +386,14 @@ Expand sections below for information on how these models were exported and test
     model = YOLO("yolov8n.pt")
 
     # TensorRT FP32
-    out = model.export(
-        format="engine",
-        imgsz:640,
-        dynamic:True,
-        verbose:False,
-        batch:8,
-        workspace:2
-    )
-    
+    out = model.export(format="engine", imgsz=640, dynamic=True, verbose=False, batch=8, workspace=2)
+
     # TensorRT FP16
+    out = model.export(format="engine", imgsz=640, dynamic=True, verbose=False, batch=8, workspace=2, half=True)
+
+    # TensorRT INT8 with calibration `data` (i.e. COCO, ImageNet, or DOTAv1 for appropriate model task)
     out = model.export(
-        format="engine",
-        imgsz:640,
-        dynamic:True,
-        verbose:False,
-        batch:8,
-        workspace:2,
-        half=True
-    )
-    
-    # TensorRT INT8
-    out = model.export(
-        format="engine",
-        imgsz:640,
-        dynamic:True,
-        verbose:False,
-        batch:8,
-        workspace:2,
-        int8=True,
-        data:"data.yaml"  # COCO, ImageNet, or DOTAv1 for appropriate model task
+        format="engine", imgsz=640, dynamic=True, verbose=False, batch=8, workspace=2, int8=True, data="coco8.yaml"
     )
     ```