why is super() used with in same class

class AnomalyDetector(Model):
  def __init__(self):
    super(AnomalyDetector, self).__init__()
    self.encoder = tf.keras.Sequential([
      layers.Dense(64, activation="relu"),
      layers.Dense(32, activation="relu"),
      layers.Dense(16, activation="relu"),
      layers.Dense(8, activation="relu")]) 
    
    self.decoder = tf.keras.Sequential([
      layers.Dense(16, activation="relu"),
      layers.Dense(32, activation="relu"),
      layers.Dense(64, activation="relu"),
      layers.Dense(140, activation="sigmoid")])
    
  def call(self, x):
    encoded = self.encoder(x)
    decoded = self.decoder(encoded)
    return decoded

i learnt that we use super() to call a parent class method inside a child class . but in this case there is nothing like parent and child , its only one class . please help me understand this code entirely.

i was not able to understand why is super() used in this

The Super() function is used to give access to the methods and properties of a parent or sibling class.

So in your case, you are inheriting the AnomalyDetector from the Model from TensorFlow Keras API that will initialize important attributes such as the input and output shapes of the model.

Further MODEL is a parent class in Keras API that contains all the model classes. So by using the super(AnomalyDetector, self).init() method of the parent model class it calls all the attributes from the AnomalyDetector subclass and this allows AnomalyDetector to inherit and use the functionality and attributes of the parent Model class.