Update broken guide links

guides/ipynb/keras_cv/writing_custom_image_augmentations_with_baseimageaugmentationlayer.ipynb

@@ -48,7 +48,6 @@
     "from tensorflow.keras import layers\n",
     "from keras_cv.utils import preprocessing as preprocessing_utils\n",
     "from keras_cv.layers import BaseImageAugmentationLayer\n",
-    "from keras_cv import bounding_boxes\n",
     "import matplotlib.pyplot as plt\n",
     ""
    ]
@@ -226,7 +225,7 @@
    "source": [
     "## Adding Random Behavior with the `FactorSampler` API.\n",
     "\n",
-    "Most of the time, an image augmentation technique should not do the same thing on every\n",
+    "Usually an image augmentation technique should not do the same thing on every\n",
     "invocation of the layer's `__call__` method.\n",
     "KerasCV offers the `FactorSampler` API to allow users to provide configurable random\n",
     "distributions."
@@ -314,7 +313,7 @@
    "source": [
     "many_elephants = tf.repeat(tf.expand_dims(elephants, axis=0), 9, axis=0)\n",
     "factor = keras_cv.NormalFactorSampler(\n",
-    "    mean=0.3, standard_deviation=0.1, min_value=0.0, max_value=1.0\n",
+    "    mean=0.3, stddev=0.1, min_value=0.0, max_value=1.0\n",
     ")\n",
     "layer = RandomBlueTint(factor=factor)\n",
     "augmented = layer(many_elephants)\n",

guides/keras_cv/writing_custom_image_augmentations_with_baseimageaugmentationlayer.py

@@ -27,7 +27,6 @@
 from tensorflow.keras import layers
 from keras_cv.utils import preprocessing as preprocessing_utils
 from keras_cv.layers import BaseImageAugmentationLayer
-from keras_cv import bounding_boxes
 import matplotlib.pyplot as plt
 
 
@@ -130,7 +129,7 @@ def augment_image(self, image, transformation=None):
 """
 ## Adding Random Behavior with the `FactorSampler` API.
 
-Most of the time, an image augmentation technique should not do the same thing on every
+Usually an image augmentation technique should not do the same thing on every
 invocation of the layer's `__call__` method.
 KerasCV offers the `FactorSampler` API to allow users to provide configurable random
 distributions.
@@ -185,7 +184,7 @@ def augment_image(self, image, transformation=None):
 
 many_elephants = tf.repeat(tf.expand_dims(elephants, axis=0), 9, axis=0)
 factor = keras_cv.NormalFactorSampler(
-    mean=0.3, standard_deviation=0.1, min_value=0.0, max_value=1.0
+    mean=0.3, stddev=0.1, min_value=0.0, max_value=1.0
 )
 layer = RandomBlueTint(factor=factor)
 augmented = layer(many_elephants)