Clean up class name and self in calls to super() (#1182)

PEP 3135 [^1] simplified the syntax for `super()` in Python 3.0 from:

    super(ClassName, self)

to the following very simple and equivalent [^2] syntax:

    super()

The current Keras codebase already requires Python 3+, and there's a mix of the
two syntax formats above, sometimes both are used in a single file.

This change simplifies the entire code base by cleaning up the remaining
explicit uses of the current class name and `self` and using the cleaner
`super()` syntax everywhere consistently.

Since the new syntax is intended to be a shorthand for the old syntax, this
change should have no semantic differences from before.

[^1]: https://peps.python.org/pep-3135/
[^2]: https://docs.python.org/3/library/functions.html#super

Author: mbrukman

examples/audio/ipynb/melgan_spectrogram_inversion.ipynb

@@ -243,7 +243,7 @@
     "            return audio\n",
     "\n",
     "    def get_config(self):\n",
-    "        config = super(MelSpec, self).get_config()\n",
+    "        config = super().get_config()\n",
     "        config.update(\n",
     "            {\n",
     "                \"frame_length\": self.frame_length,\n",

examples/audio/ipynb/wav2vec2_audiocls.ipynb

@@ -423,7 +423,7 @@
     "    \"\"\"Combines the encoder and decoder into an end-to-end model for training.\"\"\"\n",
     "\n",
     "    def __init__(self, model_checkpoint, num_classes):\n",
-    "        super(TFWav2Vec2ForAudioClassification, self).__init__()\n",
+    "        super().__init__()\n",
     "        # Instantiate the Wav2Vec 2.0 model without the Classification-Head\n",
     "        self.wav2vec2 = TFWav2Vec2Model.from_pretrained(\n",
     "            model_checkpoint, apply_spec_augment=False, from_pt=True\n",

examples/audio/md/melgan_spectrogram_inversion.md

@@ -208,7 +208,7 @@ class MelSpec(layers.Layer):
             return audio
 
     def get_config(self):
-        config = super(MelSpec, self).get_config()
+        config = super().get_config()
         config.update(
             {
                 "frame_length": self.frame_length,

examples/audio/md/wav2vec2_audiocls.md

@@ -314,7 +314,7 @@ class TFWav2Vec2ForAudioClassification(layers.Layer):
     """Combines the encoder and decoder into an end-to-end model for training."""
 
     def __init__(self, model_checkpoint, num_classes):
-        super(TFWav2Vec2ForAudioClassification, self).__init__()
+        super().__init__()
         # Instantiate the Wav2Vec 2.0 model without the Classification-Head
         self.wav2vec2 = TFWav2Vec2Model.from_pretrained(
             model_checkpoint, apply_spec_augment=False, from_pt=True

examples/audio/melgan_spectrogram_inversion.py

@@ -166,7 +166,7 @@ def call(self, audio, training=True):
             return audio
 
     def get_config(self):
-        config = super(MelSpec, self).get_config()
+        config = super().get_config()
         config.update(
             {
                 "frame_length": self.frame_length,

examples/audio/wav2vec2_audiocls.py

@@ -270,7 +270,7 @@ class TFWav2Vec2ForAudioClassification(layers.Layer):
     """Combines the encoder and decoder into an end-to-end model for training."""
 
     def __init__(self, model_checkpoint, num_classes):
-        super(TFWav2Vec2ForAudioClassification, self).__init__()
+        super().__init__()
         # Instantiate the Wav2Vec 2.0 model without the Classification-Head
         self.wav2vec2 = TFWav2Vec2Model.from_pretrained(
             model_checkpoint, apply_spec_augment=False, from_pt=True

examples/generative/conditional_gan.py

@@ -153,7 +153,7 @@
 
 class ConditionalGAN(keras.Model):
     def __init__(self, discriminator, generator, latent_dim):
-        super(ConditionalGAN, self).__init__()
+        super().__init__()
         self.discriminator = discriminator
         self.generator = generator
         self.latent_dim = latent_dim
@@ -165,7 +165,7 @@ def metrics(self):
         return [self.gen_loss_tracker, self.disc_loss_tracker]
 
     def compile(self, d_optimizer, g_optimizer, loss_fn):
-        super(ConditionalGAN, self).compile()
+        super().compile()
         self.d_optimizer = d_optimizer
         self.g_optimizer = g_optimizer
         self.loss_fn = loss_fn

examples/generative/cyclegan.py

@@ -156,7 +156,7 @@ class ReflectionPadding2D(layers.Layer):
 
     def __init__(self, padding=(1, 1), **kwargs):
         self.padding = tuple(padding)
-        super(ReflectionPadding2D, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     def call(self, input_tensor, mask=None):
         padding_width, padding_height = self.padding
@@ -401,7 +401,7 @@ def __init__(
         lambda_cycle=10.0,
         lambda_identity=0.5,
     ):
-        super(CycleGan, self).__init__()
+        super().__init__()
         self.gen_G = generator_G
         self.gen_F = generator_F
         self.disc_X = discriminator_X
@@ -426,7 +426,7 @@ def compile(
         gen_loss_fn,
         disc_loss_fn,
     ):
-        super(CycleGan, self).compile()
+        super().compile()
         self.gen_G_optimizer = gen_G_optimizer
         self.gen_F_optimizer = gen_F_optimizer
         self.disc_X_optimizer = disc_X_optimizer

examples/generative/dcgan_overriding_train_step.py

@@ -110,13 +110,13 @@
 
 class GAN(keras.Model):
     def __init__(self, discriminator, generator, latent_dim):
-        super(GAN, self).__init__()
+        super().__init__()
         self.discriminator = discriminator
         self.generator = generator
         self.latent_dim = latent_dim
 
     def compile(self, d_optimizer, g_optimizer, loss_fn):
-        super(GAN, self).compile()
+        super().compile()
         self.d_optimizer = d_optimizer
         self.g_optimizer = g_optimizer
         self.loss_fn = loss_fn

examples/generative/ipynb/conditional_gan.ipynb

@@ -256,7 +256,7 @@
     "\n",
     "class ConditionalGAN(keras.Model):\n",
     "    def __init__(self, discriminator, generator, latent_dim):\n",
-    "        super(ConditionalGAN, self).__init__()\n",
+    "        super().__init__()\n",
     "        self.discriminator = discriminator\n",
     "        self.generator = generator\n",
     "        self.latent_dim = latent_dim\n",
@@ -268,7 +268,7 @@
     "        return [self.gen_loss_tracker, self.disc_loss_tracker]\n",
     "\n",
     "    def compile(self, d_optimizer, g_optimizer, loss_fn):\n",
-    "        super(ConditionalGAN, self).compile()\n",
+    "        super().compile()\n",
     "        self.d_optimizer = d_optimizer\n",
     "        self.g_optimizer = g_optimizer\n",
     "        self.loss_fn = loss_fn\n",