create a layer in keras that dynaically adjusts shape according to size of input

layer.py

# problem: sometimes we want a network that will start with a layer equal to the number of input neurons with fewer and fewer neurons in each subsequent neuron

class DenseLayer(k.layers.Layer):
  def __init__(self):
    super().__init__()

  def build(self,input_shape):
    self.layer_list=[]
    size=input_shape[-1]
    print(size)
    for i in range(3):
      setattr(self,'%s'%i,k.layers.Dense(size,activation=k.activations.sigmoid,
                                         kernel_initializer=k.initializers.glorot_normal)) 
      # using setattr here is key -- when keras builds the layer, it scans for attributes set on the class instance.
      # You can't just stash a bunch of layers in a list.
      size=size//2 if size//2>0 else 1
      self.layer_list.append("%s"%i)
    self.out=k.layers.Dense(1)
    
  def call(self,Input):
    x=Input
    for i in self.layer_list:
      x=self.__dict__[i](x)
    x=self.out(x)
    return x