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
```