Standardize docstring (#2399)

Author: sachinprasadhs

benchmarks/vectorized_channel_shuffle.py

@@ -50,7 +50,7 @@ class OldChannelShuffle(BaseImageAugmentationLayer):
         training: A boolean argument that determines whether the call should be
             run in inference mode or training mode, defaults to True.
 
-    Usage:
+    Example:
     ```python
     (images, labels), _ = keras.datasets.cifar10.load_data()
     channel_shuffle = keras_cv.layers.ChannelShuffle()

benchmarks/vectorized_grayscale.py

@@ -42,7 +42,7 @@ class OldGrayscale(BaseImageAugmentationLayer):
             after the `Grayscale` operation:
                  a. (..., height, width, 1) if output_channels = 1
                  b. (..., height, width, 3) if output_channels = 3.
-    Usage:
+    Example:
     ```python
     (images, labels), _ = keras.datasets.cifar10.load_data()
     to_grayscale = keras_cv.layers.preprocessing.Grayscale()

benchmarks/vectorized_jittered_resize.py

@@ -53,7 +53,7 @@ class OldJitteredResize(BaseImageAugmentationLayer):
     are translated and scaled according to the random scaling and random
     cropping.
 
-    Usage:
+    Example:
     ```python
     train_ds = load_object_detection_dataset()
     jittered_resize = layers.JitteredResize(

benchmarks/vectorized_mosaic.py

@@ -66,7 +66,7 @@ class OldMosaic(BaseImageAugmentationLayer):
         - [Yolov5 implementation](https://github.com/ultralytics/yolov5).
         - [YoloX implementation](https://github.com/Megvii-BaseDetection/YOLOX)
 
-    Sample usage:
+    Example:
     ```python
     (images, labels), _ = keras.datasets.cifar10.load_data()
     labels = tf.one_hot(labels,10)

benchmarks/vectorized_random_color_jitter.py

@@ -72,7 +72,7 @@ class OldRandomColorJitter(BaseImageAugmentationLayer):
             pass a tuple with two identical floats: `(0.5, 0.5)`.
         seed: Integer. Used to create a random seed.
 
-    Usage:
+    Example:
     ```python
     (images, labels), _ = keras.datasets.cifar10.load_data()
     color_jitter = keras_cv.layers.RandomColorJitter(

keras_cv/bounding_box/converters.py

@@ -338,7 +338,7 @@ def convert_format(
     performance loss, as each image will need to be processed separately due to
     the mismatching image shapes.
 
-    Usage:
+    Example:
 
     ```python
     boxes = load_coco_dataset()

keras_cv/bounding_box/to_ragged.py

@@ -29,7 +29,7 @@ def to_ragged(bounding_boxes, sentinel=-1, dtype=tf.float32):
     checking for padded sentinel value of the class_id axis of the
     bounding_boxes.
 
-    Usage:
+    Example:
     ```python
     bounding_boxes = {
         "boxes": tf.constant([[2, 3, 4, 5], [0, 1, 2, 3]]),

keras_cv/core/factor_sampler/constant_factor_sampler.py

@@ -29,7 +29,7 @@ class ConstantFactorSampler(FactorSampler):
     Args:
         value: the value to return from `__call__()`.
 
-    Usage:
+    Example:
     ```python
     constant_factor = keras_cv.ConstantFactorSampler(0.5)
     random_sharpness = keras_cv.layers.RandomSharpness(factor=constant_factor)

keras_cv/core/factor_sampler/normal_factor_sampler.py

@@ -31,7 +31,7 @@ class NormalFactorSampler(FactorSampler):
         min_value: values below min_value are clipped to min_value.
         max_value: values above max_value are clipped to max_value.
 
-    Usage:
+    Example:
     ```python
     factor = keras_cv.core.NormalFactor(
         mean=0.5,

keras_cv/core/factor_sampler/uniform_factor_sampler.py

@@ -31,7 +31,7 @@ class UniformFactorSampler(FactorSampler):
         seed: A shape int or Tensor, the seed to the random number generator.
             Must have dtype int32 or int64. (When using XLA, only int32 is
             allowed.)
-    Usage:
+    Example:
     ```python
     uniform_factor = keras_cv.UniformFactorSampler(0, 0.5)
     random_sharpness = keras_cv.layers.RandomSharpness(factor=uniform_factor)