Minor fix in docs for python admonition and code blocks (#16646)

docs/en/guides/isolating-segmentation-objects.md

@@ -141,7 +141,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 
         === "Black Background Pixels"
 
-            ```py
+            ```python
             # Create 3-channel mask
             mask3ch = cv2.cvtColor(b_mask, cv2.COLOR_GRAY2BGR)
 
@@ -192,7 +192,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 
         === "Transparent Background Pixels"
 
-            ```py
+            ```python
             # Isolate object with transparent background (when saved as PNG)
             isolated = np.dstack([img, b_mask])
             ```
@@ -248,7 +248,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 
     ??? example "Example Final Step"
 
-        ```py
+        ```python
         # Save isolated object to file
         _ = cv2.imwrite(f"{img_name}_{label}-{ci}.png", iso_crop)
         ```

docs/en/hub/datasets.md

@@ -48,7 +48,7 @@ The dataset YAML is the same standard YOLOv5 and YOLOv8 YAML format.
 
 After zipping your dataset, you should [validate it](https://docs.ultralytics.com/reference/hub/__init__/#ultralytics.hub.check_dataset) before uploading it to [Ultralytics HUB](https://www.ultralytics.com/hub). [Ultralytics HUB](https://www.ultralytics.com/hub) conducts the dataset validation check post-upload, so by ensuring your dataset is correctly formatted and error-free ahead of time, you can forestall any setbacks due to dataset rejection.
 
-```py
+```python
 from ultralytics.hub import check_dataset
 
 check_dataset("path/to/dataset.zip", task="detect")

docs/en/integrations/tensorrt.md

@@ -380,7 +380,7 @@ Expand sections below for information on how these models were exported and test
 
     See [export mode](../modes/export.md) for details regarding export configuration arguments.
 
-    ```py
+    ```python
     from ultralytics import YOLO
 
     model = YOLO("yolov8n.pt")
@@ -401,7 +401,7 @@ Expand sections below for information on how these models were exported and test
 
     See [predict mode](../modes/predict.md) for additional information.
 
-    ```py
+    ```python
     import cv2
 
     from ultralytics import YOLO
@@ -421,7 +421,7 @@ Expand sections below for information on how these models were exported and test
 
     See [`val` mode](../modes/val.md) to learn more about validation configuration arguments.
 
-    ```py
+    ```python
     from ultralytics import YOLO
 
     model = YOLO("yolov8n.engine")

docs/en/usage/python.md

@@ -306,26 +306,26 @@ Explorer API can be used to explore datasets with advanced semantic, vector-simi
 
 !!! tip "Detection Trainer Example"
 
-        ```python
+    ```python
-        from ultralytics.models.yolo import DetectionPredictor, DetectionTrainer, DetectionValidator
+    from ultralytics.models.yolo import DetectionPredictor, DetectionTrainer, DetectionValidator
 
-        # trainer
+    # trainer
-        trainer = DetectionTrainer(overrides={})
+    trainer = DetectionTrainer(overrides={})
-        trainer.train()
+    trainer.train()
-        trained_model = trainer.best
+    trained_model = trainer.best
 
-        # Validator
+    # Validator
-        val = DetectionValidator(args=...)
+    val = DetectionValidator(args=...)
-        val(model=trained_model)
+    val(model=trained_model)
 
-        # predictor
+    # predictor
-        pred = DetectionPredictor(overrides={})
+    pred = DetectionPredictor(overrides={})
-        pred(source=SOURCE, model=trained_model)
+    pred(source=SOURCE, model=trained_model)
 
-        # resume from last weight
+    # resume from last weight
-        overrides["resume"] = trainer.last
+    overrides["resume"] = trainer.last
-        trainer = detect.DetectionTrainer(overrides=overrides)
+    trainer = detect.DetectionTrainer(overrides=overrides)
-        ```
+    ```
 
 You can easily customize Trainers to support custom tasks or explore R&D ideas. Learn more about Customizing `Trainers`, `Validators` and `Predictors` to suit your project needs in the Customization Section.