Understanding declared parameters in my Conv2d layer of my convolutional neural network I am trying to understand the architecture of my keras model implemented by the sequential model.
Here is a piece of the code :
model = Sequential([
    #block1
    layers.Conv2D(nfilter,(3,3),padding=&quot;same&quot;,name=&quot;block1_conv1&quot;,input_shape=(64,64,3)),
    layers.Activation(&quot;relu&quot;),
    layers.BatchNormalization(), .......

My question is why the two parameters input_shape and name are declared in the layer conv2D while they are not included in the set of defined parmeters for Conv2D() in this link https://keras.io/api/layers/convolution_layers/convolution2d/
Bot the name and input_shape come from the Layer class which Conv2D inherited. In the doc you provide, they are implicitly in **kwargs