A neural network layer.
Types that conform to
Layer represent functions that map inputs to outputs. They may have an
internal state represented by parameters, such as weight tensors.
Layer instances define a differentiable call method for mapping inputs to outputs.
Returns the inference output and the backpropagation function obtained from applying the layer to the given input.
The input to the layer.
A tuple containing the output and the backpropagation function. The backpropagation function (a.k.a. backpropagator) takes a direction vector and returns the gradients at the layer and at the input, respectively.