Once a custom layer—whether parameterless or equipped with learnable parameters—is defined, it can be seamlessly integrated as a standard component within more complex neural network architectures. For instance, it can be added to a sequential model alongside standard built-in layers. In this configuration, the custom layer receives the output from the preceding component, applies its predefined transformation during the forward pass, and passes the resulting activations to the subsequent layers in the sequence.