Apply new Ruff actions to Python codeblocks (#13783)

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

docs/en/guides/sahi-tiled-inference.md

@@ -60,12 +60,6 @@ pip install -U ultralytics sahi
 Here's how to import the necessary modules and download a YOLOv8 model and some test images:
 
 ```python
-from pathlib import Path
-
-from IPython.display import Image
-from sahi import AutoDetectionModel
-from sahi.predict import get_prediction, get_sliced_prediction, predict
-from sahi.utils.cv import read_image
 from sahi.utils.file import download_from_url
 from sahi.utils.yolov8 import download_yolov8s_model
 
@@ -91,6 +85,8 @@ download_from_url(
 You can instantiate a YOLOv8 model for object detection like this:
 
 ```python
+from sahi import AutoDetectionModel
+
 detection_model = AutoDetectionModel.from_pretrained(
     model_type="yolov8",
     model_path=yolov8_model_path,
@@ -104,6 +100,8 @@ detection_model = AutoDetectionModel.from_pretrained(
 Perform standard inference using an image path or a numpy image.
 
 ```python
+from sahi.predict import get_prediction
+
 # With an image path
 result = get_prediction("demo_data/small-vehicles1.jpeg", detection_model)
 
@@ -125,6 +123,8 @@ Image("demo_data/prediction_visual.png")
 Perform sliced inference by specifying the slice dimensions and overlap ratios:
 
 ```python
+from sahi.predict import get_sliced_prediction
+
 result = get_sliced_prediction(
     "demo_data/small-vehicles1.jpeg",
     detection_model,
@@ -155,6 +155,8 @@ result.to_fiftyone_detections()[:3]
 For batch prediction on a directory of images:
 
 ```python
+from sahi.predict import predict
+
 predict(
     model_type="yolov8",
     model_path="path/to/yolov8n.pt",