Hi, I want to initialize my class(torch.autograd.Function), with some variables, however, I could not see any example like that. When I try to initialize the object with  __init__(), the forward pass cannot find those variables. Is it possible to do so?
Bellow is the only example I could find, where the class MyRelu doesn’t have any initial paramateres.
import torch

class MyReLU(torch.autograd.Function):
    """
    We can implement our own custom autograd Functions by subclassing
    torch.autograd.Function and implementing the forward and backward passes
    which operate on Tensors.
    """
   #how can I initialize the class with some variables here?
   #I've tried this without success
   #  def __init__(ctx, alpha=0.5, beta=0.5, gamma=1.0):
   #    ctx.alpha = alpha
   #    ctx.beta = beta
   #    ctx.gama = gama
    @staticmethod
    def forward(ctx, input):
        """
        In the forward pass we receive a Tensor containing the input and return
        a Tensor containing the output. ctx is a context object that can be used
        to stash information for backward computation. You can cache arbitrary
        objects for use in the backward pass using the ctx.save_for_backward method.
        """
        ctx.save_for_backward(input)
        return input.clamp(min=0)

    @staticmethod
    def backward(ctx, grad_output):
        """
        In the backward pass we receive a Tensor containing the gradient of the loss
        with respect to the output, and we need to compute the gradient of the loss
        with respect to the input.
        """
        input, = ctx.saved_tensors
        grad_input = grad_output.clone()
        grad_input[input < 0] = 0
        return grad_input