Fix docstrings and args default values

keras_hub/src/models/mit/mit_backbone.py

@@ -57,7 +57,7 @@ def __init__(
         ```python
         images = np.ones(shape=(1, 96, 96, 3))
         labels = np.zeros(shape=(1, 96, 96, 1))
-        backbone = keras_hub.models.MiTBackbone.from_preset("mit_b0_imagenet")
+        backbone = keras_hub.models.MiTBackbone.from_preset("mit_b0_ade20k_512")
 
         # Evaluate model
         model(images)

keras_hub/src/models/mobilenet/mobilenet_backbone.py

@@ -96,7 +96,7 @@ def __init__(
         stackwise_activation,
         output_num_filters,
         inverted_res_block,
-        image_shape=(224, 224, 3),
+        image_shape=(None, None, 3),
         input_activation="hard_swish",
         output_activation="hard_swish",
         depth_multiplier=1.0,

keras_hub/src/models/resnet/resnet_backbone.py

@@ -68,7 +68,7 @@ class ResNetBackbone(FeaturePyramidBackbone):
     input_data = np.random.uniform(0, 1, size=(2, 224, 224, 3))
 
     # Pretrained ResNet backbone.
-    model = keras_hub.models.ResNetBackbone.from_preset("resnet50")
+    model = keras_hub.models.ResNetBackbone.from_preset("resnet_50_imagenet")
     model(input_data)
 
     # Randomly initialized ResNetV2 backbone with a custom config.

keras_hub/src/models/vae/vae_backbone.py

@@ -10,7 +10,7 @@
 
 
 class VAEBackbone(Backbone):
-    """VAE backbone used in latent diffusion models.
+    """Variational Autoencoder(VAE) backbone used in latent diffusion models.
 
     When encoding, this model generates mean and log variance of the input
     images. When decoding, it reconstructs images from the latent space.
@@ -51,6 +51,18 @@ class VAEBackbone(Backbone):
             `"channels_last"`.
         dtype: `None` or str or `keras.mixed_precision.DTypePolicy`. The dtype
             to use for the model's computations and weights.
+
+    Example:
+    ```Python
+    backbone = VAEBackbone(
+        encoder_num_filters=[32, 32, 32, 32],
+        encoder_num_blocks=[1, 1, 1, 1],
+        decoder_num_filters=[32, 32, 32, 32],
+        decoder_num_blocks=[1, 1, 1, 1],
+    )
+    input_data = ops.ones((2, self.height, self.width, 3))
+    output = backbone(input_data)
+    ```
     """
 
     def __init__(

keras_hub/src/models/vgg/vgg_backbone.py

@@ -20,7 +20,7 @@ class VGGBackbone(Backbone):
       stackwise_num_filters: list of ints, filter size for convolutional
             blocks per VGG block. For both VGG16 and VGG19 this is [
             64, 128, 256, 512, 512].
-      image_shape: tuple, optional shape tuple, defaults to (224, 224, 3).
+      image_shape: tuple, optional shape tuple, defaults to (None, None, 3).
 
     Examples:
     ```python