@Forceless
As far as i could find, in the torch documentation for conv2d, the weights of conv2d
https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html#torch.nn.Conv2d

import math
import torch
from torch.autograd import grad
import torch.nn as nn

m = nn.Conv2d(16, 33, 3, stride=2)
input = torch.randn(20, 16, 50, 100)
output = m(input)

# According to the equation
k = math.sqrt(1.0/(16 * 3 *3))
print([-k, k])

# UNIT TEST
print([torch.min(m.weight), torch.max(m.weight)])

[-0.08333333333333333, 0.08333333333333333]
[tensor(-0.0833, grad_fn=<MinBackward1>), tensor(0.0833, grad_fn=<MaxBackward1>)]