diff --git a/src/supervision/annotators/core.py b/src/supervision/annotators/core.py
index 38c2957e..af844099 100644
--- a/src/supervision/annotators/core.py
+++ b/src/supervision/annotators/core.py
@@ -201,6 +201,7 @@ class BoxAnnotator(BaseAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -214,6 +215,8 @@ class BoxAnnotator(BaseAnnotator):
             ...     detections=detections
             ... )
 
+            ```
+
         ![bounding-box-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/bounding-box-annotator-example-purple.png)
         """
@@ -376,6 +379,7 @@ class MaskAnnotator(BaseAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -390,6 +394,8 @@ class MaskAnnotator(BaseAnnotator):
             ...     detections=detections
             ... )
 
+            ```
+
         ![mask-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/mask-annotator-example-purple.png)
         """
@@ -468,6 +474,7 @@ class PolygonAnnotator(BaseAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -481,6 +488,8 @@ class PolygonAnnotator(BaseAnnotator):
             ...     detections=detections
             ... )
 
+            ```
+
         ![polygon-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/polygon-annotator-example-purple.png)
         """
@@ -556,6 +565,7 @@ class ColorAnnotator(BaseAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -569,6 +579,8 @@ class ColorAnnotator(BaseAnnotator):
             ...     detections=detections
             ... )
 
+            ```
+
         ![box-mask-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/box-mask-annotator-example-purple.png)
         """
@@ -653,6 +665,7 @@ class HaloAnnotator(BaseAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -666,6 +679,8 @@ class HaloAnnotator(BaseAnnotator):
             ...     detections=detections
             ... )
 
+            ```
+
         ![halo-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/halo-annotator-example-purple.png)
         """
@@ -754,6 +769,7 @@ class EllipseAnnotator(BaseAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -767,6 +783,8 @@ class EllipseAnnotator(BaseAnnotator):
             ...     detections=detections
             ... )
 
+            ```
+
         ![ellipse-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/ellipse-annotator-example-purple.png)
         """
@@ -847,6 +865,7 @@ class BoxCornerAnnotator(BaseAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -860,6 +879,8 @@ class BoxCornerAnnotator(BaseAnnotator):
             ...     detections=detections
             ... )
 
+            ```
+
         ![box-corner-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/box-corner-annotator-example-purple.png)
         """
@@ -937,6 +958,7 @@ class CircleAnnotator(BaseAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -950,6 +972,8 @@ class CircleAnnotator(BaseAnnotator):
             ...     detections=detections
             ... )
 
+            ```
+
 
         ![circle-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/circle-annotator-example-purple.png)
@@ -1037,6 +1061,7 @@ class DotAnnotator(BaseAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -1050,6 +1075,8 @@ class DotAnnotator(BaseAnnotator):
             ...     detections=detections
             ... )
 
+            ```
+
         ![dot-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/dot-annotator-example-purple.png)
         """
@@ -1166,6 +1193,7 @@ class LabelAnnotator(_BaseLabelAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -1187,6 +1215,8 @@ class LabelAnnotator(_BaseLabelAnnotator):
             ...     labels=labels
             ... )
 
+            ```
+
         ![label-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/label-annotator-example-purple.png)
         """
@@ -1481,6 +1511,7 @@ class RichLabelAnnotator(_BaseLabelAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> from PIL import Image
@@ -1502,6 +1533,8 @@ class RichLabelAnnotator(_BaseLabelAnnotator):
             ...     detections=detections,
             ...     labels=labels
             ... )
+
+            ```
         """
         assert isinstance(scene, Image.Image)
         validate_labels(labels, detections)
@@ -1805,6 +1838,7 @@ class BlurAnnotator(BaseAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -1818,6 +1852,8 @@ class BlurAnnotator(BaseAnnotator):
             ...     detections=detections
             ... )
 
+            ```
+
         ![blur-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/blur-annotator-example-purple.png)
         """
@@ -2207,6 +2243,7 @@ class TriangleAnnotator(BaseAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -2220,6 +2257,8 @@ class TriangleAnnotator(BaseAnnotator):
             ...     detections=detections
             ... )
 
+            ```
+
         ![triangle-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/triangle-annotator-example.png)
         """
@@ -2321,6 +2360,7 @@ class RoundBoxAnnotator(BaseAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -2334,6 +2374,8 @@ class RoundBoxAnnotator(BaseAnnotator):
             ...     detections=detections
             ... )
 
+            ```
+
         ![round-box-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/round-box-annotator-example-purple.png)
         """
@@ -2469,6 +2511,7 @@ class PercentageBarAnnotator(BaseAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -2483,6 +2526,8 @@ class PercentageBarAnnotator(BaseAnnotator):
             ...     detections=detections
             ... )
 
+            ```
+
         ![percentage-bar-example](https://media.roboflow.com/
         supervision-annotator-examples/percentage-bar-annotator-example-purple.png)
         """
@@ -2648,6 +2693,7 @@ class CropAnnotator(BaseAnnotator):
             The annotated image.
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -2661,6 +2707,8 @@ class CropAnnotator(BaseAnnotator):
             ...     detections=detections
             ... )
 
+            ```
+
         ![crop-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/crop-annotator-example.png)
         """
@@ -2790,6 +2838,7 @@ class BackgroundOverlayAnnotator(BaseAnnotator):
                 or `PIL.Image.Image`)
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -2803,6 +2852,8 @@ class BackgroundOverlayAnnotator(BaseAnnotator):
             ...     detections=detections
             ... )
 
+            ```
+
         ![background-overlay-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/background-color-annotator-example-purple.png)
         """
diff --git a/src/supervision/annotators/utils.py b/src/supervision/annotators/utils.py
index 7fc5bfad..6bb6417b 100644
--- a/src/supervision/annotators/utils.py
+++ b/src/supervision/annotators/utils.py
@@ -273,10 +273,9 @@ def snap_boxes(
         A numpy array of shape `(N, 4)` with boxes shifted into frame.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> from supervision.annotators.utils import snap_boxes
-
-        Example boxes:
         >>> xyxy = np.array([
         ...     [-10, 10, 30, 50],     # Off left edge
         ...     [310, 200, 350, 250],  # Off right edge
@@ -294,6 +293,8 @@ def snap_boxes(
                [200., 190., 250., 240.],
                [  0.,  10., 370.,  50.],
                [ 10.,   0.,  30., 280.]], dtype=float32)
+
+        ```
     """
     result = np.copy(xyxy)
     width, height = resolution_wh
diff --git a/src/supervision/assets/downloader.py b/src/supervision/assets/downloader.py
index d507b643..e535a95a 100644
--- a/src/supervision/assets/downloader.py
+++ b/src/supervision/assets/downloader.py
@@ -46,9 +46,12 @@ def download_assets(asset_name: VideoAssets | str) -> str:
         The filename of the downloaded asset.
 
     Example:
+        ```pycon
         >>> from supervision.assets import download_assets, VideoAssets
         >>> download_assets(VideoAssets.VEHICLES)  # doctest: +SKIP
         'vehicles.mp4'
+
+        ```
     """
 
     filename = asset_name.value if isinstance(asset_name, VideoAssets) else asset_name
diff --git a/src/supervision/classification/core.py b/src/supervision/classification/core.py
index 86703b2f..32f38722 100644
--- a/src/supervision/classification/core.py
+++ b/src/supervision/classification/core.py
@@ -167,6 +167,7 @@ class Classifications:
                 the top k class IDs and confidences.
 
         Example:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> classifications = sv.Classifications(
@@ -175,6 +176,8 @@ class Classifications:
             ... )
             >>> classifications.get_top_k(1)
             (array([1]), array([0.9]))
+
+            ```
         """
         if self.confidence is None:
             raise ValueError("top_k could not be calculated, confidence is None")
diff --git a/src/supervision/dataset/core.py b/src/supervision/dataset/core.py
index d901195a..f0b3d339 100644
--- a/src/supervision/dataset/core.py
+++ b/src/supervision/dataset/core.py
@@ -165,6 +165,7 @@ class DetectionDataset(BaseDataset):
                 the training and testing datasets.
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> ds = sv.DetectionDataset(
@@ -181,6 +182,8 @@ class DetectionDataset(BaseDataset):
             >>> train_ds, test_ds = ds.split(split_ratio=0.5, random_state=42)
             >>> len(train_ds), len(test_ds)
             (1, 1)
+
+            ```
         """
 
         train_paths, test_paths = train_test_split(
@@ -232,6 +235,7 @@ class DetectionDataset(BaseDataset):
             the merged data from the input list.
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> ds_1 = sv.DetectionDataset(
@@ -257,6 +261,8 @@ class DetectionDataset(BaseDataset):
             2
             >>> ds_merged.classes
             ['cat', 'dog', 'person']
+
+            ```
         """
 
         def is_in_memory(dataset: DetectionDataset) -> bool:
@@ -760,6 +766,7 @@ class ClassificationDataset(BaseDataset):
             the training and testing datasets.
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> cd = sv.ClassificationDataset(
@@ -776,6 +783,8 @@ class ClassificationDataset(BaseDataset):
             >>> train_cd, test_cd = cd.split(split_ratio=0.5, random_state=42)
             >>> len(train_cd), len(test_cd)
             (1, 1)
+
+            ```
         """
         train_paths, test_paths = train_test_split(
             data=self.image_paths,
diff --git a/src/supervision/dataset/utils.py b/src/supervision/dataset/utils.py
index 8cf4682f..37d9bf91 100644
--- a/src/supervision/dataset/utils.py
+++ b/src/supervision/dataset/utils.py
@@ -163,6 +163,7 @@ def rle_to_mask(
             number of pixels in the expected mask (computed based on resolution_wh).
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> mask = sv.rle_to_mask([5, 2, 2, 2, 5], (4, 4))
@@ -171,6 +172,8 @@ def rle_to_mask(
                [0, 1, 1, 0],
                [0, 1, 1, 0],
                [0, 0, 0, 0]], dtype=uint8)
+
+        ```
     """
     if isinstance(rle, list):
         rle = np.array(rle, dtype=int)
@@ -210,6 +213,7 @@ def mask_to_rle(mask: npt.NDArray[np.bool_]) -> list[int]:
         AssertionError: If input mask is not 2D or is empty.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> mask = np.array([
@@ -222,6 +226,9 @@ def mask_to_rle(mask: npt.NDArray[np.bool_]) -> list[int]:
         >>> [int(x) for x in rle]
         [0, 16]
 
+        ```
+
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> mask = np.array([
@@ -234,6 +241,8 @@ def mask_to_rle(mask: npt.NDArray[np.bool_]) -> list[int]:
         >>> [int(x) for x in rle]
         [5, 2, 2, 2, 5]
 
+        ```
+
     ![mask_to_rle](https://media.roboflow.com/supervision-docs/
     mask-to-rle.png){ align=center width="800" }
     """
diff --git a/src/supervision/detection/tools/csv_sink.py b/src/supervision/detection/tools/csv_sink.py
index a81899f5..d8be7d73 100644
--- a/src/supervision/detection/tools/csv_sink.py
+++ b/src/supervision/detection/tools/csv_sink.py
@@ -34,6 +34,7 @@ class CSVSink:
             Defaults to 'output.csv'.
 
     Example:
+        ```pycon
         >>> import supervision as sv
         >>> import numpy as np
         >>> import tempfile
@@ -53,6 +54,8 @@ class CSVSink:
         >>> with csv_sink as sink:
         ...     sink.append(detections, custom_data={'frame': 0})
         >>> os.unlink(temp_file.name)  # Clean up
+
+        ```
     """
 
     def __init__(self, file_name: str = "output.csv") -> None:
diff --git a/src/supervision/detection/tools/smoother.py b/src/supervision/detection/tools/smoother.py
index bab98305..32d80e59 100644
--- a/src/supervision/detection/tools/smoother.py
+++ b/src/supervision/detection/tools/smoother.py
@@ -30,6 +30,7 @@ class DetectionsSmoother:
         - This class is not compatible with segmentation models.
 
     Example:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> smoother = sv.DetectionsSmoother(length=3)
@@ -52,6 +53,8 @@ class DetectionsSmoother:
         >>> smoothed.confidence
         array([0.6])
 
+        ```
+
 
         ```python
         import supervision as sv
diff --git a/src/supervision/detection/utils/boxes.py b/src/supervision/detection/utils/boxes.py
index af0bccf5..c736adbb 100644
--- a/src/supervision/detection/utils/boxes.py
+++ b/src/supervision/detection/utils/boxes.py
@@ -23,6 +23,7 @@ def clip_boxes(xyxy: np.ndarray, resolution_wh: tuple[int, int]) -> np.ndarray:
             within the frame resolution.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> xyxy = np.array([
@@ -34,6 +35,8 @@ def clip_boxes(xyxy: np.ndarray, resolution_wh: tuple[int, int]) -> np.ndarray:
         array([[ 10,  20, 300, 200],
                [ 15,  25, 320, 240],
                [  0,   0,  30,  40]])
+
+        ```
     """
     result = np.copy(xyxy)
     width, height = resolution_wh
@@ -62,6 +65,7 @@ def pad_boxes(xyxy: np.ndarray, px: int, py: int | None = None) -> np.ndarray:
             values.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> xyxy = np.array([
@@ -71,6 +75,8 @@ def pad_boxes(xyxy: np.ndarray, px: int, py: int | None = None) -> np.ndarray:
         >>> sv.pad_boxes(xyxy=xyxy, px=5, py=10)
         array([[ 5, 10, 35, 50],
                [10, 15, 40, 55]])
+
+        ```
     """
     if py is None:
         py = px
@@ -107,6 +113,7 @@ def denormalize_boxes(
             `(x_min, y_min, x_max, y_max)` format.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> xyxy = np.array([
@@ -119,11 +126,16 @@ def denormalize_boxes(
                [384., 288., 896., 576.],
                [256.,  72., 768., 360.]])
 
+        ```
+
+        ```pycon
         >>> xyxy = np.array([
         ...     [256., 128., 768., 640.]
         ... ])
         >>> sv.denormalize_boxes(xyxy, (1280, 720), normalization_factor=1024.0)
         array([[320.,  90., 960., 450.]])
+
+        ```
     """
     width, height = resolution_wh
     result = xyxy.copy()
@@ -148,6 +160,7 @@ def move_boxes(
         Repositioned bounding boxes.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> xyxy = np.array([
@@ -158,6 +171,8 @@ def move_boxes(
         >>> sv.move_boxes(xyxy=xyxy, offset=offset)
         array([[15, 15, 25, 25],
                [35, 35, 45, 45]])
+
+        ```
     """
     return xyxy + np.hstack([offset, offset])
 
@@ -177,6 +192,7 @@ def move_oriented_boxes(
         Repositioned bounding boxes.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> from supervision.detection.utils.boxes import move_oriented_boxes
         >>> xyxyxyxy = np.array([
@@ -204,6 +220,8 @@ def move_oriented_boxes(
                 [25, 45],
                 [35, 55],
                 [45, 45]]])
+
+        ```
     """
     return xyxyxyxy + offset
 
@@ -225,6 +243,7 @@ def scale_boxes(
         Scaled bounding boxes.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> xyxy = np.array([
@@ -234,6 +253,8 @@ def scale_boxes(
         >>> sv.scale_boxes(xyxy=xyxy, factor=1.5)
         array([[ 7.5,  7.5, 22.5, 22.5],
                [27.5, 27.5, 42.5, 42.5]])
+
+        ```
     """
     centers = (xyxy[:, :2] + xyxy[:, 2:]) / 2
     new_sizes = (xyxy[:, 2:] - xyxy[:, :2]) * factor
@@ -252,6 +273,7 @@ def spread_out_boxes(
         max_iterations: Maximum number of iterations to run the algorithm for.
 
     Example:
+        ```pycon
         >>> import numpy as np
         >>> from supervision.detection.utils.boxes import spread_out_boxes
         >>> xyxy = np.array([
@@ -264,6 +286,8 @@ def spread_out_boxes(
         True
         >>> bool(spread_out[1, 0] > 12 and spread_out[1, 1] > 12)
         True
+
+        ```
     """
     if len(xyxy) == 0:
         return xyxy
diff --git a/src/supervision/detection/utils/converters.py b/src/supervision/detection/utils/converters.py
index ad051293..cc886cff 100644
--- a/src/supervision/detection/utils/converters.py
+++ b/src/supervision/detection/utils/converters.py
@@ -55,6 +55,7 @@ def xywh_to_xyxy(xywh: np.ndarray) -> np.ndarray:
             to a bounding box in the format `(x_min, y_min, x_max, y_max)`.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> xywh = np.array([
@@ -64,6 +65,8 @@ def xywh_to_xyxy(xywh: np.ndarray) -> np.ndarray:
         >>> sv.xywh_to_xyxy(xywh=xywh)
         array([[10, 20, 40, 60],
                [15, 25, 50, 70]])
+
+        ```
     """
     xyxy = xywh.copy()
     xyxy[:, 2] = xywh[:, 0] + xywh[:, 2]
@@ -86,6 +89,7 @@ def xyxy_to_xywh(xyxy: np.ndarray) -> np.ndarray:
             to a bounding box in the format `(x, y, width, height)`.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> xyxy = np.array([
@@ -95,6 +99,8 @@ def xyxy_to_xywh(xyxy: np.ndarray) -> np.ndarray:
         >>> sv.xyxy_to_xywh(xyxy=xyxy)
         array([[10, 20, 30, 40],
                [15, 25, 35, 45]])
+
+        ```
     """
     xywh = xyxy.copy()
     xywh[:, 2] = xyxy[:, 2] - xyxy[:, 0]
@@ -117,6 +123,7 @@ def xcycwh_to_xyxy(xcycwh: np.ndarray) -> np.ndarray:
             to a bounding box in the format `(x_min, y_min, x_max, y_max)`.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> xcycwh = np.array([
@@ -126,6 +133,8 @@ def xcycwh_to_xyxy(xcycwh: np.ndarray) -> np.ndarray:
         >>> sv.xcycwh_to_xyxy(xcycwh=xcycwh)
         array([[40. , 35. , 60. , 65. ],
                [25. , 32.5, 35. , 47.5]])
+
+        ```
     """
     xyxy = xcycwh.copy()
     xyxy[:, 0] = xcycwh[:, 0] - xcycwh[:, 2] / 2
@@ -149,6 +158,7 @@ def xyxy_to_xcycarh(xyxy: np.ndarray) -> np.ndarray:
             `(center x, center y, aspect ratio, height)`. Shape `(N, 4)`.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> xyxy = np.array([
@@ -159,6 +169,8 @@ def xyxy_to_xcycarh(xyxy: np.ndarray) -> np.ndarray:
         array([[25.        , 40.        ,  0.75      , 40.        ],
                [32.5       , 47.5       ,  0.77..., 45.        ]])
 
+        ```
+
     """
     if xyxy.size == 0:
         return np.empty((0, 4), dtype=float)
@@ -219,6 +231,7 @@ def xyxy_to_mask(boxes: np.ndarray, resolution_wh: tuple[int, int]) -> np.ndarra
             for each bounding box.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> boxes = np.array([[0, 0, 2, 2]])
@@ -241,6 +254,8 @@ def xyxy_to_mask(boxes: np.ndarray, resolution_wh: tuple[int, int]) -> np.ndarra
                 [False, False, False, False, False],
                 [False, False, False,  True,  True],
                 [False, False, False,  True,  True]]])
+
+        ```
     """
     width, height = resolution_wh
     n = boxes.shape[0]
diff --git a/src/supervision/detection/utils/iou_and_nms.py b/src/supervision/detection/utils/iou_and_nms.py
index 6e1b4ded..36ef0e35 100644
--- a/src/supervision/detection/utils/iou_and_nms.py
+++ b/src/supervision/detection/utils/iou_and_nms.py
@@ -111,6 +111,7 @@ def box_iou(
         ValueError: If `overlap_metric` is not IOU or IOS.
 
     Examples:
+        ```pycon
         >>> import supervision as sv
         >>> box_true = [100, 100, 200, 200]
         >>> box_detection = [150, 150, 250, 250]
@@ -118,6 +119,8 @@ def box_iou(
         0.142857...
         >>> sv.box_iou(box_true, box_detection, overlap_metric=sv.OverlapMetric.IOS)
         0.25
+
+        ```
     """
     overlap_metric = OverlapMetric.from_value(overlap_metric)
     x_min_true, y_min_true, x_max_true, y_max_true = np.array(box_true)
@@ -184,6 +187,7 @@ def box_iou_batch(
         ValueError: If `overlap_metric` is not IOU or IOS.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> boxes_true = np.array([
@@ -204,6 +208,8 @@ def box_iou_batch(
         ... )
         array([[0.25, 0.  ],
                [0.  , 0.64]], dtype=float32)
+
+        ```
     """
     overlap_metric = OverlapMetric.from_value(overlap_metric)
     x_min_true, y_min_true, x_max_true, y_max_true = boxes_true.T
@@ -311,6 +317,7 @@ def box_iou_batch_with_jaccard(
         np.ndarray: Array of IoU values of shape (len(dt), len(gt)).
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> boxes_true = [
@@ -330,6 +337,8 @@ def box_iou_batch_with_jaccard(
         >>> ious  # doctest: +ELLIPSIS
         array([[0.886..., 0.496...],
                [0.4  ..., 0.862...]])
+
+        ```
     """
     assert len(is_crowd) == len(boxes_true), (
         "`is_crowd` must have the same length as `boxes_true`"
diff --git a/src/supervision/detection/utils/masks.py b/src/supervision/detection/utils/masks.py
index 9b4193ed..a03bb2bf 100644
--- a/src/supervision/detection/utils/masks.py
+++ b/src/supervision/detection/utils/masks.py
@@ -30,6 +30,7 @@ def move_masks(
             shape.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> mask = np.array([[[False, False, False, False],
@@ -48,6 +49,8 @@ def move_masks(
                 [False, False, False, False],
                 [False, False, False, False],
                 [ True, False, False, False]]])
+
+        ```
     """
     mask_array = np.full((masks.shape[0], resolution_wh[1], resolution_wh[0]), False)
 
@@ -126,6 +129,7 @@ def contains_holes(mask: npt.NDArray[np.bool_]) -> bool:
         True if holes are detected, False otherwise.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> mask = np.array([
@@ -147,6 +151,8 @@ def contains_holes(mask: npt.NDArray[np.bool_]) -> bool:
         >>> sv.contains_holes(mask=mask)
         False
 
+        ```
+
     ![contains_holes](https://media.roboflow.com/supervision-docs/contains-holes.png){ align=center width="800" }
     """  # noqa E501 // docs
     mask_uint8 = mask.astype(np.uint8)
@@ -182,6 +188,7 @@ def contains_multiple_segments(
         ValueError: If connectivity(int) parameter value is not 4 or 8.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> mask = np.array([
@@ -205,6 +212,8 @@ def contains_multiple_segments(
         >>> sv.contains_multiple_segments(mask=mask, connectivity=4)
         False
 
+        ```
+
     ![contains_multiple_segments](https://media.roboflow.com/supervision-docs/contains-multiple-segments.png){ align=center width="800" }
     """  # noqa E501 // docs
     if connectivity != 4 and connectivity != 8:
@@ -287,6 +296,7 @@ def filter_segments_by_distance(
         Boolean mask after filtering.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> mask = np.array([
@@ -322,6 +332,8 @@ def filter_segments_by_distance(
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
 
+        ```
+
         The nearby 2×2 block at columns 6–7 is kept because its edge distance
         is within 3 pixels. The distant block at columns 9-10 is removed.
     """  # noqa E501 // docs
diff --git a/src/supervision/detection/utils/vlms.py b/src/supervision/detection/utils/vlms.py
index d48a360e..ccc32205 100644
--- a/src/supervision/detection/utils/vlms.py
+++ b/src/supervision/detection/utils/vlms.py
@@ -18,6 +18,7 @@ def edit_distance(string_1: str, string_2: str, case_sensitive: bool = True) ->
         into `string_2`.
 
     Examples:
+        ```pycon
         >>> import supervision as sv
         >>> sv.edit_distance("hello", "hello")
         0
@@ -31,6 +32,8 @@ def edit_distance(string_1: str, string_2: str, case_sensitive: bool = True) ->
         0
         >>> sv.edit_distance("hello world", "helloworld")
         1
+
+        ```
     """
     if not case_sensitive:
         string_1 = string_1.lower()
@@ -80,6 +83,7 @@ def fuzzy_match_index(
             or None if no match is found.
 
     Examples:
+        ```pycon
         >>> from supervision.detection.utils.vlms import fuzzy_match_index
         >>> fuzzy_match_index(["cat", "dog", "rat"], "dat", threshold=1)
         0
@@ -87,6 +91,8 @@ def fuzzy_match_index(
         1
         >>> fuzzy_match_index(["one", "two", "three"], "xyz", threshold=2) is None
         True
+
+        ```
     """
     for idx, candidate in enumerate(candidates):
         if edit_distance(candidate, query, case_sensitive=case_sensitive) <= threshold:
diff --git a/src/supervision/draw/color.py b/src/supervision/draw/color.py
index 008a3839..9ed448e3 100644
--- a/src/supervision/draw/color.py
+++ b/src/supervision/draw/color.py
@@ -76,10 +76,13 @@ class Color:
         b (int): Blue channel value (0-255).
 
     Example:
+        ```pycon
         >>> import supervision as sv
         >>> sv.Color.WHITE
         Color(r=255, g=255, b=255)
 
+        ```
+
     | Constant   | Hex Code   | RGB               |
     |------------|------------|-------------------|
     | `WHITE`    | `#FFFFFF`  | `(255, 255, 255)` |
@@ -111,11 +114,14 @@ class Color:
             Color: An instance representing the color.
 
         Example:
+            ```pycon
             >>> import supervision as sv
             >>> sv.Color.from_hex('#ff00ff')
             Color(r=255, g=0, b=255)
             >>> sv.Color.from_hex('#f0f')
             Color(r=255, g=0, b=255)
+
+            ```
         """
         _validate_color_hex(color_hex)
         color_hex = color_hex.lstrip("#")
@@ -137,9 +143,12 @@ class Color:
             Color: An instance representing the color.
 
         Example:
+            ```pycon
             >>> import supervision as sv
             >>> sv.Color.from_rgb_tuple((255, 255, 0))
             Color(r=255, g=255, b=0)
+
+            ```
         """
         r, g, b = color_tuple
         return cls(r=r, g=g, b=b)
@@ -157,9 +166,12 @@ class Color:
             Color: An instance representing the color.
 
         Example:
+            ```pycon
             >>> import supervision as sv
             >>> sv.Color.from_bgr_tuple((0, 255, 255))
             Color(r=255, g=255, b=0)
+
+            ```
         """
         b, g, r = color_tuple
         return cls(r=r, g=g, b=b)
@@ -172,9 +184,12 @@ class Color:
             str: The hexadecimal color string.
 
         Example:
+            ```pycon
             >>> import supervision as sv
             >>> sv.Color(r=255, g=255, b=0).as_hex()
             '#ffff00'
+
+            ```
         """
         return f"#{self.r:02x}{self.g:02x}{self.b:02x}"
 
@@ -186,9 +201,12 @@ class Color:
             Tuple[int, int, int]: RGB tuple.
 
         Example:
+            ```pycon
             >>> import supervision as sv
             >>> sv.Color(r=255, g=255, b=0).as_rgb()
             (255, 255, 0)
+
+            ```
         """
         return self.r, self.g, self.b
 
@@ -200,9 +218,12 @@ class Color:
             Tuple[int, int, int]: BGR tuple.
 
         Example:
+            ```pycon
             >>> import supervision as sv
             >>> sv.Color(r=255, g=255, b=0).as_bgr()
             (0, 255, 255)
+
+            ```
         """
         return self.b, self.g, self.r
 
@@ -263,10 +284,13 @@ class ColorPalette:
             ColorPalette: A ColorPalette instance with default colors.
 
         Example:
+            ```pycon
             >>> import supervision as sv
             >>> sv.ColorPalette.DEFAULT  # doctest: +ELLIPSIS
             ColorPalette(colors=[Color(r=163, g=81, b=251), Color(r=255, g=64, b=64), ...])
 
+            ```
+
         ![default-color-palette](https://media.roboflow.com/
         supervision-annotator-examples/default-color-palette.png)
         """  # noqa: E501 // docs
@@ -281,10 +305,13 @@ class ColorPalette:
             ColorPalette: A ColorPalette instance with Roboflow colors.
 
         Example:
+            ```pycon
             >>> import supervision as sv
             >>> sv.ColorPalette.ROBOFLOW  # doctest: +ELLIPSIS
             ColorPalette(colors=[Color(r=194, g=141, b=252), Color(r=163, g=81, b=251), ...])
 
+            ```
+
         ![roboflow-color-palette](https://media.roboflow.com/
         supervision-annotator-examples/roboflow-color-palette.png)
         """  # noqa: E501 // docs
@@ -306,10 +333,13 @@ class ColorPalette:
             ColorPalette: A ColorPalette instance.
 
         Example:
+            ```pycon
             >>> import supervision as sv
             >>> colors = ['#ff0000', '#00ff00', '#0000ff']
             >>> sv.ColorPalette.from_hex(colors)  # doctest: +ELLIPSIS
             ColorPalette(colors=[Color(r=255, g=0, b=0), Color(r=0, g=255, b=0), ...])
+
+            ```
         """
         colors = [Color.from_hex(color_hex) for color_hex in color_hex_list]
         return cls(colors)
@@ -327,10 +357,13 @@ class ColorPalette:
             ColorPalette: A ColorPalette instance.
 
         Example:
+            ```pycon
             >>> import supervision as sv
             >>> sv.ColorPalette.from_matplotlib('viridis', 5)  # doctest: +ELLIPSIS
             ColorPalette(colors=[Color(r=68, g=1, b=84), Color(r=58, g=82, b=139), ...])
 
+            ```
+
         ![visualized_color_palette](https://media.roboflow.com/
         supervision-annotator-examples/visualized_color_palette.png)
         """
@@ -356,11 +389,14 @@ class ColorPalette:
             Color: Color at the given index.
 
         Example:
+            ```pycon
             >>> import supervision as sv
             >>> colors = ['#ff0000', '#00ff00', '#0000ff']
             >>> color_palette = sv.ColorPalette.from_hex(colors)
             >>> color_palette.by_idx(1)
             Color(r=0, g=255, b=0)
+
+            ```
         """
         if idx < 0:
             raise ValueError("idx argument should not be negative")
@@ -389,11 +425,14 @@ def unify_to_bgr(color: tuple[int, int, int] | Color) -> tuple[int, int, int]:
         The color in BGR format as a tuple of three integers.
 
     Example:
+        ```pycon
         >>> from supervision.draw.color import unify_to_bgr, Color
         >>> unify_to_bgr(Color.WHITE)
         (255, 255, 255)
         >>> unify_to_bgr((0, 255, 255))
         (0, 255, 255)
+
+        ```
     """
     if issubclass(type(color), Color):
         color = cast(Color, color)
diff --git a/src/supervision/draw/utils.py b/src/supervision/draw/utils.py
index a6dca7b7..3109d579 100644
--- a/src/supervision/draw/utils.py
+++ b/src/supervision/draw/utils.py
@@ -248,6 +248,7 @@ def draw_text(
         np.ndarray: The input scene with the text drawn on it.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> from supervision.geometry.core import Point
         >>> from supervision.draw.utils import draw_text
@@ -258,6 +259,8 @@ def draw_text(
         ... )
         >>> scene.shape
         (100, 100, 3)
+
+        ```
     """
     text_width, text_height = cv2.getTextSize(
         text=text,
@@ -376,11 +379,14 @@ def calculate_optimal_text_scale(resolution_wh: tuple[int, int]) -> float:
         float: recommended font scale factor
 
     Examples:
+        ```pycon
         >>> import supervision as sv
         >>> sv.calculate_optimal_text_scale((1920, 1080))
         1.08
         >>> sv.calculate_optimal_text_scale((640, 480))
         0.48
+
+        ```
     """
     return min(resolution_wh) * 1e-3
 
@@ -398,11 +404,14 @@ def calculate_optimal_line_thickness(resolution_wh: tuple[int, int]) -> int:
         int: recommended line thickness in pixels
 
     Examples:
+        ```pycon
         >>> import supervision as sv
         >>> sv.calculate_optimal_line_thickness((1920, 1080))
         4
         >>> sv.calculate_optimal_line_thickness((640, 480))
         2
+
+        ```
     """
     if min(resolution_wh) < 1080:
         return 2
diff --git a/src/supervision/geometry/core.py b/src/supervision/geometry/core.py
index fc8fb63d..cc9b647a 100644
--- a/src/supervision/geometry/core.py
+++ b/src/supervision/geometry/core.py
@@ -36,12 +36,15 @@ class Point:
         y: The y-coordinate of the point.
 
     Example:
+        ```pycon
         >>> from supervision.geometry.core import Point
         >>> point = Point(x=10.0, y=20.0)
         >>> point.as_xy_int_tuple()
         (10, 20)
         >>> point.as_xy_float_tuple()
         (10.0, 20.0)
+
+        ```
     """
 
     x: float
@@ -76,6 +79,7 @@ class Vector:
         end: The end point of the vector.
 
     Example:
+        ```pycon
         >>> from supervision.geometry.core import Point, Vector
         >>> start_point = Point(x=0.0, y=0.0)
         >>> end_point = Point(x=3.0, y=4.0)
@@ -84,6 +88,8 @@ class Vector:
         5.0
         >>> vector.center
         Point(x=1.5, y=2.0)
+
+        ```
     """
 
     start: Point
@@ -148,6 +154,7 @@ class Rect:
         height: The height of the rectangle.
 
     Example:
+        ```pycon
         >>> from supervision.geometry.core import Rect
         >>> rect = Rect(x=10.0, y=20.0, width=30.0, height=40.0)
         >>> rect.top_left
@@ -156,6 +163,8 @@ class Rect:
         Point(x=40.0, y=60.0)
         >>> rect.as_xyxy_int_tuple()
         (10, 20, 40, 60)
+
+        ```
     """
 
     x: float
diff --git a/src/supervision/geometry/utils.py b/src/supervision/geometry/utils.py
index f51e4577..e9a95a31 100644
--- a/src/supervision/geometry/utils.py
+++ b/src/supervision/geometry/utils.py
@@ -21,6 +21,7 @@ def get_polygon_center(polygon: npt.NDArray[np.float64]) -> Point:
         ValueError: If the polygon has no vertices.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> polygon = np.array([[0, 0], [0, 2], [2, 2], [2, 0]])
@@ -29,6 +30,8 @@ def get_polygon_center(polygon: npt.NDArray[np.float64]) -> Point:
         1.0
         >>> float(center.y)
         1.0
+
+        ```
     """
 
     # This is one of the 3 candidate algorithms considered for centroid calculation.
diff --git a/src/supervision/key_points/annotators.py b/src/supervision/key_points/annotators.py
index dc004abb..a13115b1 100644
--- a/src/supervision/key_points/annotators.py
+++ b/src/supervision/key_points/annotators.py
@@ -61,6 +61,7 @@ class VertexAnnotator(BaseKeyPointAnnotator):
                 or `PIL.Image.Image`)
 
         Example:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -77,6 +78,8 @@ class VertexAnnotator(BaseKeyPointAnnotator):
             ... )
             >>> annotated_frame.shape
             (100, 100, 3)
+
+            ```
         """
         assert isinstance(scene, np.ndarray)
         if len(key_points) == 0:
@@ -137,6 +140,7 @@ class EdgeAnnotator(BaseKeyPointAnnotator):
                     or `PIL.Image.Image`)
 
         Example:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -153,6 +157,8 @@ class EdgeAnnotator(BaseKeyPointAnnotator):
             ... )
             >>> annotated_frame.shape
             (100, 100, 3)
+
+            ```
         """
         assert isinstance(scene, np.ndarray)
         if len(key_points) == 0:
@@ -248,6 +254,7 @@ class VertexLabelAnnotator:
                 or `PIL.Image.Image`)
 
         Example:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -266,6 +273,8 @@ class VertexLabelAnnotator:
             >>> annotated_frame.shape
             (100, 100, 3)
 
+            ```
+
         ![vertex-label-annotator-example](https://media.roboflow.com/supervision-annotator-examples/vertex-label-annotator-example.png)
 
         !!! tip
@@ -274,6 +283,7 @@ class VertexLabelAnnotator:
             values.
 
         Example:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -312,6 +322,8 @@ class VertexLabelAnnotator:
             >>> annotated_frame.shape
             (100, 100, 3)
 
+            ```
+
         ![vertex-label-annotator-custom-example](https://media.roboflow.com/supervision-annotator-examples/vertex-label-annotator-custom-example.png)
         """
         assert isinstance(scene, np.ndarray)
diff --git a/src/supervision/key_points/core.py b/src/supervision/key_points/core.py
index 417b03c1..4099d0dd 100644
--- a/src/supervision/key_points/core.py
+++ b/src/supervision/key_points/core.py
@@ -175,12 +175,15 @@ class KeyPoints:
             int: The number of objects.
 
         Example:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> xy = np.array([[[10, 20], [30, 40]]], dtype=np.float32)
             >>> key_points = sv.KeyPoints(xy=xy)
             >>> len(key_points)
             1
+
+            ```
         """
         return len(self.xy)
 
@@ -769,10 +772,13 @@ class KeyPoints:
             An empty `sv.KeyPoints` object.
 
         Examples:
+            ```pycon
             >>> import supervision as sv
             >>> key_points = sv.KeyPoints.empty()
             >>> len(key_points)
             0
+
+            ```
         """
         return cls(xy=np.empty((0, 0, 2), dtype=np.float32))
 
@@ -784,10 +790,13 @@ class KeyPoints:
             bool: `True` if the object is empty, `False` otherwise.
 
         Example:
+            ```pycon
             >>> import supervision as sv
             >>> key_points = sv.KeyPoints.empty()
             >>> key_points.is_empty()
             True
+
+            ```
         """
         empty_key_points = KeyPoints.empty()
         empty_key_points.data = self.data
@@ -811,6 +820,7 @@ class KeyPoints:
             detections: The converted detections object.
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> key_points = sv.KeyPoints(
@@ -819,6 +829,8 @@ class KeyPoints:
             >>> detections = key_points.as_detections()
             >>> detections.xyxy
             array([[10., 20., 30., 40.]], dtype=float32)
+
+            ```
         """
         if self.is_empty():
             return Detections.empty()
diff --git a/src/supervision/metrics/detection.py b/src/supervision/metrics/detection.py
index 7074474e..b057d7f5 100644
--- a/src/supervision/metrics/detection.py
+++ b/src/supervision/metrics/detection.py
@@ -121,6 +121,7 @@ class ConfusionMatrix:
             New instance of ConfusionMatrix.
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> targets = [
@@ -144,6 +145,8 @@ class ConfusionMatrix:
             >>> confusion_matrix.matrix
             array([[1., 0.],
                    [0., 0.]])
+
+            ```
         """
 
         prediction_tensors = []
@@ -192,6 +195,7 @@ class ConfusionMatrix:
             New instance of ConfusionMatrix.
 
         Examples:
+            ```pycon
             >>> import supervision as sv
             >>> import numpy as np
             >>> targets = [
@@ -217,6 +221,8 @@ class ConfusionMatrix:
             array([[1., 0., 1.],
                    [0., 1., 0.],
                    [1., 0., 0.]])
+
+            ```
         """
         validate_input_tensors(predictions, targets)
 
@@ -533,6 +539,7 @@ class MeanAveragePrecision:
             New instance of ConfusionMatrix.
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> targets = [
@@ -554,6 +561,8 @@ class MeanAveragePrecision:
             ... )
             >>> round(float(mAP.map50), 2)
             0.99
+
+            ```
         """
         prediction_tensors = []
         target_tensors = []
@@ -637,6 +646,7 @@ class MeanAveragePrecision:
             New instance of MeanAveragePrecision.
 
         Examples:
+            ```pycon
             >>> import supervision as sv
             >>> import numpy as np
             >>> targets = [
@@ -659,6 +669,8 @@ class MeanAveragePrecision:
             ... )
             >>> round(float(mAP.map50), 2)
             0.81
+
+            ```
         """
         validate_input_tensors(predictions, targets)
         iou_thresholds = np.linspace(0.5, 0.95, 10)
diff --git a/src/supervision/metrics/f1_score.py b/src/supervision/metrics/f1_score.py
index e34a614e..ecfe701c 100644
--- a/src/supervision/metrics/f1_score.py
+++ b/src/supervision/metrics/f1_score.py
@@ -38,6 +38,7 @@ class F1Score(Metric):
     `F1 = 2 * (precision * recall) / (precision + recall)`
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> from supervision.metrics import F1Score
@@ -55,6 +56,8 @@ class F1Score(Metric):
         >>> round(float(f1_result.f1_50), 2)
         1.0
 
+        ```
+
     ![example_plot](
         https://media.roboflow.com/supervision-docs/metrics/f1_plot_example.png
     ){ align=center width="800" }
diff --git a/src/supervision/metrics/mean_average_precision.py b/src/supervision/metrics/mean_average_precision.py
index 69ab21fc..24c4cc49 100644
--- a/src/supervision/metrics/mean_average_precision.py
+++ b/src/supervision/metrics/mean_average_precision.py
@@ -1188,6 +1188,7 @@ class MeanAveragePrecision(Metric):
     It is the average of the precision-recall curves at different IoU thresholds.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> from supervision.metrics import MeanAveragePrecision
@@ -1212,6 +1213,8 @@ class MeanAveragePrecision(Metric):
         Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
         Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
 
+        ```
+
     ![example_plot](
         https://media.roboflow.com/supervision-docs/metrics/mAP_plot_example.png
     ){ align=center width="800" }
diff --git a/src/supervision/metrics/mean_average_recall.py b/src/supervision/metrics/mean_average_recall.py
index 670b07a7..3d937178 100644
--- a/src/supervision/metrics/mean_average_recall.py
+++ b/src/supervision/metrics/mean_average_recall.py
@@ -252,6 +252,7 @@ class MeanAverageRecall(Metric):
     confidence detection for each class.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> from supervision.metrics import MeanAverageRecall
@@ -269,6 +270,8 @@ class MeanAverageRecall(Metric):
         >>> round(float(mar_result.mAR_at_100), 2)
         1.0
 
+        ```
+
     ![example_plot](
         https://media.roboflow.com/supervision-docs/metrics/mAR_plot_example.png
     ){ align=center width="800" }
diff --git a/src/supervision/metrics/precision.py b/src/supervision/metrics/precision.py
index 596a6f74..c0c9c1e1 100644
--- a/src/supervision/metrics/precision.py
+++ b/src/supervision/metrics/precision.py
@@ -41,6 +41,7 @@ class Precision(Metric):
     number of false positive detections (detected, but incorrectly).
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> from supervision.metrics import Precision
@@ -58,6 +59,8 @@ class Precision(Metric):
         >>> round(float(precision_result.precision_at_50), 2)
         1.0
 
+        ```
+
     ![example_plot](
         https://media.roboflow.com/supervision-docs/metrics/precision_plot_example.png
     ){ align=center width="800" }
diff --git a/src/supervision/metrics/recall.py b/src/supervision/metrics/recall.py
index d16791ab..ef7b7c97 100644
--- a/src/supervision/metrics/recall.py
+++ b/src/supervision/metrics/recall.py
@@ -41,6 +41,7 @@ class Recall(Metric):
     number of false negatives (missed detections).
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> from supervision.metrics import Recall
@@ -58,6 +59,8 @@ class Recall(Metric):
         >>> round(float(recall_result.recall_at_50), 2)
         1.0
 
+        ```
+
     ![example_plot](
         https://media.roboflow.com/supervision-docs/metrics/recall_plot_example.png
     ){ align=center width="800" }
diff --git a/src/supervision/metrics/utils/object_size.py b/src/supervision/metrics/utils/object_size.py
index 8e9457c4..ad9f37b5 100644
--- a/src/supervision/metrics/utils/object_size.py
+++ b/src/supervision/metrics/utils/object_size.py
@@ -24,6 +24,7 @@ class ObjectSizeCategory(Enum):
     Large: area >= 96^2
 
     Example:
+        ```pycon
         >>> from supervision.metrics.utils.object_size import ObjectSizeCategory
         >>> ObjectSizeCategory.SMALL.value
         1
@@ -31,6 +32,8 @@ class ObjectSizeCategory(Enum):
         2
         >>> ObjectSizeCategory.LARGE.value
         3
+
+        ```
     """
 
     ANY = -1
@@ -55,6 +58,7 @@ def get_object_size_category(
         the enum values of ObjectSizeCategory. Shaped (N,).
 
     Example:
+        ```pycon
         >>> import numpy as np
         >>> from supervision.metrics.core import MetricTarget
         >>> from supervision.metrics.utils.object_size import get_object_size_category
@@ -65,6 +69,8 @@ def get_object_size_category(
         ... ])
         >>> get_object_size_category(xyxy, MetricTarget.BOXES)
         array([1, 2, 3])
+
+        ```
     """
     if metric_target == MetricTarget.BOXES:
         return get_bbox_size_category(data)
@@ -87,6 +93,7 @@ def get_bbox_size_category(xyxy: npt.NDArray[np.float32]) -> npt.NDArray[np.int_
         the enum values of ObjectSizeCategory. Shaped (N,).
 
     Example:
+        ```pycon
         >>> import numpy as np
         >>> from supervision.metrics.utils.object_size import get_bbox_size_category
         >>> xyxy = np.array([
@@ -97,6 +104,8 @@ def get_bbox_size_category(xyxy: npt.NDArray[np.float32]) -> npt.NDArray[np.int_
         ... ])
         >>> get_bbox_size_category(xyxy)
         array([1, 2, 2, 3])
+
+        ```
     """
     if len(xyxy.shape) != 2 or xyxy.shape[1] != 4:
         raise ValueError("Bounding boxes must be shaped (N, 4)")
@@ -125,6 +134,7 @@ def get_mask_size_category(mask: npt.NDArray[np.bool_]) -> npt.NDArray[np.int_]:
         the enum values of ObjectSizeCategory. Shaped (N,).
 
     Example:
+        ```pycon
         >>> import numpy as np
         >>> from supervision.metrics.utils.object_size import get_mask_size_category
         >>> mask = np.zeros((3, 100, 100), dtype=bool)
@@ -133,6 +143,8 @@ def get_mask_size_category(mask: npt.NDArray[np.bool_]) -> npt.NDArray[np.int_]:
         >>> mask[2, 0:100, 0:100] = True # 10000 (Large)
         >>> get_mask_size_category(mask)
         array([1, 2, 3])
+
+        ```
     """
     if len(mask.shape) != 3:
         raise ValueError("Masks must be shaped (N, H, W)")
@@ -159,6 +171,7 @@ def get_obb_size_category(xyxyxyxy: npt.NDArray[np.float32]) -> npt.NDArray[np.i
         the enum values of ObjectSizeCategory. Shaped (N,).
 
     Example:
+        ```pycon
         >>> import numpy as np
         >>> from supervision.metrics.utils.object_size import get_obb_size_category
         >>> obb = np.array([
@@ -168,6 +181,8 @@ def get_obb_size_category(xyxyxyxy: npt.NDArray[np.float32]) -> npt.NDArray[np.i
         ... ])
         >>> get_obb_size_category(obb)
         array([1, 2, 3])
+
+        ```
     """
     if len(xyxyxyxy.shape) != 3 or xyxyxyxy.shape[1] != 4 or xyxyxyxy.shape[2] != 2:
         raise ValueError("Oriented bounding boxes must be shaped (N, 4, 2)")
diff --git a/src/supervision/utils/file.py b/src/supervision/utils/file.py
index 4849ae20..764ee5f3 100644
--- a/src/supervision/utils/file.py
+++ b/src/supervision/utils/file.py
@@ -35,6 +35,7 @@ def list_files_with_extensions(
         A list of Path objects for the matching files.
 
     Examples:
+        ```pycon
         >>> import supervision as sv
         >>> from pathlib import Path
         >>> import tempfile
@@ -54,6 +55,8 @@ def list_files_with_extensions(
         ...     directory=tmpdir, extensions=['txt', 'md'])
         >>> len(files)
         2
+
+        ```
     """
 
     directory = Path(directory)
@@ -82,6 +85,7 @@ def read_txt_file(file_path: str | Path, skip_empty: bool = False) -> list[str]:
         A list of strings representing the lines in the text file.
 
     Examples:
+        ```pycon
         >>> import tempfile
         >>> from pathlib import Path
         >>> from supervision.utils.file import read_txt_file, save_text_file
@@ -92,6 +96,8 @@ def read_txt_file(file_path: str | Path, skip_empty: bool = False) -> list[str]:
         ...     print(read_txt_file(file_path, skip_empty=True))
         ['line1', ' ', 'line3']
         ['line1', 'line3']
+
+        ```
     """
     with open(str(file_path)) as file:
         if skip_empty:
@@ -126,6 +132,7 @@ def read_json_file(file_path: str | Path) -> dict[str, Any]:
         A dict of annotations information
 
     Examples:
+        ```pycon
         >>> import tempfile
         >>> from pathlib import Path
         >>> from supervision.utils.file import read_json_file, save_json_file
@@ -135,6 +142,8 @@ def read_json_file(file_path: str | Path) -> dict[str, Any]:
         ...     save_json_file(data, file_path)
         ...     print(read_json_file(file_path))
         {'key': 'value', 'list': [1, 2, 3]}
+
+        ```
     """
     with open(str(file_path)) as file:
         data = json.load(file)
diff --git a/src/supervision/utils/image.py b/src/supervision/utils/image.py
index d8417225..717cd5a1 100644
--- a/src/supervision/utils/image.py
+++ b/src/supervision/utils/image.py
@@ -35,6 +35,7 @@ def crop_image(
             type.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> image = np.zeros((1080, 1920, 3), dtype=np.uint8)
@@ -45,6 +46,9 @@ def crop_image(
         >>> cropped_image.shape
         (400, 400, 3)
 
+        ```
+
+        ```pycon
         >>> image = np.zeros((1920, 1080), dtype=np.uint8)
         >>> image.shape
         (1920, 1080)
@@ -53,6 +57,8 @@ def crop_image(
         >>> cropped_image.shape
         (400, 400)
 
+        ```
+
     ![crop-image](https://media.roboflow.com/supervision-docs/supervision-docs-crop-image-2.png){ align=center width="1000" }
     """  # noqa E501 // docs
     if isinstance(xyxy, (list, tuple)):
@@ -89,6 +95,7 @@ def scale_image(image: ImageType, scale_factor: float) -> ImageType:
         ValueError: If scale factor is non-positive.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> image = np.zeros((1080, 1920, 3), dtype=np.uint8)
@@ -98,6 +105,9 @@ def scale_image(image: ImageType, scale_factor: float) -> ImageType:
         >>> scaled_image.shape
         (540, 960, 3)
 
+        ```
+
+        ```pycon
         >>> image = np.zeros((1920, 1080), dtype=np.uint8)
         >>> image.shape
         (1920, 1080)
@@ -105,6 +115,8 @@ def scale_image(image: ImageType, scale_factor: float) -> ImageType:
         >>> scaled_image.shape
         (960, 540)
 
+        ```
+
     ![scale-image](https://media.roboflow.com/supervision-docs/supervision-docs-scale-image-2.png){ align=center width="1000" }
     """  # noqa E501 // docs
     if scale_factor <= 0:
@@ -136,6 +148,7 @@ def resize_image(
             type.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> image = np.zeros((1080, 1920, 3), dtype=np.uint8)
@@ -147,6 +160,9 @@ def resize_image(
         >>> resized_image.shape
         (562, 1000, 3)
 
+        ```
+
+        ```pycon
         >>> image = np.zeros((1920, 1080), dtype=np.uint8)
         >>> image.shape
         (1920, 1080)
@@ -156,6 +172,8 @@ def resize_image(
         >>> resized_image.shape
         (1000, 562)
 
+        ```
+
     ![resize-image](https://media.roboflow.com/supervision-docs/supervision-docs-resize-image-2.png){ align=center width="1000" }
     """  # noqa E501 // docs
     if keep_aspect_ratio:
@@ -194,6 +212,7 @@ def letterbox_image(
             type.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> image = np.zeros((1080, 1920, 3), dtype=np.uint8)
@@ -205,6 +224,8 @@ def letterbox_image(
         >>> letterboxed_image.shape
         (1000, 1000, 3)
 
+        ```
+
     ![letterbox-image](https://media.roboflow.com/supervision-docs/supervision-docs-letterbox-image-2.png){ align=center width="1000" }
     """  # noqa E501 // docs
     assert isinstance(image, np.ndarray)
@@ -260,6 +281,7 @@ def overlay_image(
         Scene with overlay applied, shape `(height, width, 3)`.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> image = np.zeros((1000, 1000, 3), dtype=np.uint8)
@@ -270,6 +292,8 @@ def overlay_image(
         ... )
         >>> result_image.shape
         (1000, 1000, 3)
+
+        ```
     """
     scene_height, scene_width = image.shape[:2]
     image_height, image_width = overlay.shape[:2]
@@ -332,6 +356,7 @@ def tint_image(
         ValueError: If opacity is outside range [0.0, 1.0].
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -341,6 +366,8 @@ def tint_image(
         >>> tinted_image.shape
         (100, 100, 3)
 
+        ```
+
     ![tint-image](https://media.roboflow.com/supervision-docs/supervision-docs-tint-image-2.png){ align=center width="1000" }
     """  # noqa E501 // docs
     if not 0.0 <= opacity <= 1.0:
@@ -368,6 +395,7 @@ def grayscale_image(image: ImageType) -> ImageType:
             matching input type.
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> image = np.ones((100, 100, 3), dtype=np.uint8) * 128
@@ -375,6 +403,8 @@ def grayscale_image(image: ImageType) -> ImageType:
         >>> grayscale_image.shape
         (100, 100, 3)
 
+        ```
+
     ![grayscale-image](https://media.roboflow.com/supervision-docs/supervision-docs-grayscale-image-2.png){ align=center width="1000" }
     """  # noqa E501 // docs
     grayscaled = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
@@ -400,11 +430,14 @@ def get_image_resolution_wh(image: ImageType) -> tuple[int, int]:
             `PIL.Image.Image`).
 
     Examples:
+        ```pycon
         >>> import numpy as np
         >>> import supervision as sv
         >>> image = np.zeros((1080, 1920, 3), dtype=np.uint8)
         >>> sv.get_image_resolution_wh(image)
         (1920, 1080)
+
+        ```
     """
     if isinstance(image, np.ndarray):
         if image.ndim < 2:
@@ -444,6 +477,7 @@ class ImageSink:
                 Defaults to `"image_{:05d}.png"`.
 
         Examples:
+            ```pycon
             >>> import numpy as np
             >>> import supervision as sv
             >>> import tempfile
@@ -456,6 +490,8 @@ class ImageSink:
             ...     files = sorted(os.listdir(tmpdir))
             ...     len(files)
             2
+
+            ```
         """
         self.target_dir_path = target_dir_path
         self.overwrite = overwrite
diff --git a/src/supervision/utils/internal.py b/src/supervision/utils/internal.py
index c93cc07e..4b92c43b 100644
--- a/src/supervision/utils/internal.py
+++ b/src/supervision/utils/internal.py
@@ -78,6 +78,7 @@ def deprecated_parameter(
             parameter as deprecated.
 
     Examples:
+        ```pycon
         >>> from supervision.utils.internal import deprecated_parameter
         >>> import warnings
         >>> @deprecated_parameter(
@@ -94,6 +95,8 @@ def deprecated_parameter(
         ...     result = example_function(old_name='deprecated_value')
         ...     print(result)
         deprecated_value
+
+        ```
     """
 
     def decorator(func: Callable[..., Any]) -> Callable[..., Any]:
@@ -196,6 +199,7 @@ def get_instance_variables(instance: Any, include_properties: bool = False) -> s
         include_properties: Whether to include properties in the result
 
     Usage:
+        ```pycon
         >>> from supervision.utils.internal import get_instance_variables
         >>> import numpy as np
         >>> from supervision import Detections
@@ -205,6 +209,8 @@ def get_instance_variables(instance: Any, include_properties: bool = False) -> s
         True
         >>> 'data' in variables
         True
+
+        ```
     """
     if isinstance(instance, type):
         raise ValueError("Only class instances are supported, not classes.")
diff --git a/src/supervision/utils/iterables.py b/src/supervision/utils/iterables.py
index 268887b7..9b30e6bb 100644
--- a/src/supervision/utils/iterables.py
+++ b/src/supervision/utils/iterables.py
@@ -22,11 +22,14 @@ def create_batches(
             the input `sequence`.
 
     Examples:
+        ```pycon
         >>> from supervision.utils.iterables import create_batches
         >>> list(create_batches([1, 2, 3, 4, 5], 2))
         [[1, 2], [3, 4], [5]]
         >>> list(create_batches("abcde", 3))
         [['a', 'b', 'c'], ['d', 'e']]
+
+        ```
     """
     batch_size = max(batch_size, 1)
     current_batch: list[V] = []
@@ -56,11 +59,14 @@ def fill(sequence: list[V], desired_size: int, content: V) -> list[V]:
         A padded version of the input `sequence` (if needed).
 
     Examples:
+        ```pycon
         >>> from supervision.utils.iterables import fill
         >>> fill([1, 2], 4, 0)
         [1, 2, 0, 0]
         >>> fill(['a', 'b'], 3, 'c')
         ['a', 'b', 'c']
+
+        ```
     """
     missing_size = max(0, desired_size - len(sequence))
     sequence.extend([content] * missing_size)
@@ -78,11 +84,14 @@ def find_duplicates(sequence: list[V]) -> list[V]:
         A list of duplicate elements found in the sequence.
 
     Examples:
+        ```pycon
         >>> from supervision.utils.iterables import find_duplicates
         >>> sorted(find_duplicates([1, 2, 3, 2, 4, 5, 1]))
         [1, 2]
         >>> find_duplicates(['a', 'b', 'c'])
         []
+
+        ```
     """
     seen = set()
     duplicates = set()
diff --git a/src/supervision/utils/notebook.py b/src/supervision/utils/notebook.py
index a0f61513..ed8f4f68 100644
--- a/src/supervision/utils/notebook.py
+++ b/src/supervision/utils/notebook.py
@@ -21,6 +21,7 @@ def plot_image(
         cmap: the colormap to use for single channel images.
 
     Examples:
+        ```pycon
         >>> import cv2
         >>> import numpy as np
         >>> import matplotlib
@@ -29,6 +30,8 @@ def plot_image(
         >>> image = np.zeros((100, 100, 3), dtype=np.uint8)
         >>> sv.plot_image(image=image, size=(16, 16))
         ...
+
+        ```
     """
     if isinstance(image, Image.Image):
         image = pillow_to_cv2(image)
@@ -69,6 +72,7 @@ def plot_images_grid(
        ValueError: If the number of images exceeds the grid size.
 
     Examples:
+        ```pycon
         >>> import cv2
         >>> import numpy as np
         >>> import matplotlib
@@ -82,6 +86,8 @@ def plot_images_grid(
         >>> titles = ["Image 1", "Image 2", "Image 3"]
         >>> sv.plot_images_grid(images, grid_size=(2, 2), titles=titles, size=(16, 16))
         ...
+
+        ```
     """
     nrows, ncols = grid_size
 
diff --git a/src/supervision/validators/__init__.py b/src/supervision/validators/__init__.py
index 2ece1db4..bb31816d 100644
--- a/src/supervision/validators/__init__.py
+++ b/src/supervision/validators/__init__.py
@@ -6,7 +6,10 @@ import numpy as np
 def validate_xyxy(xyxy: Any) -> None:
     """Validate that xyxy is a 2D np.ndarray with shape (N, 4).
 
+    ```pycon
     >>> validate_xyxy(np.array([[0, 0, 1, 1], [1, 1, 2, 2]]))
+
+    ```
     """
     expected_shape = "(_, 4)"
     actual_shape = str(getattr(xyxy, "shape", None))