class_docstr: Apply swap noise on the input data.
  
  Each data point has specified chance be replaced by a random value from the same column.
code: def forward(self, x):
      should_swap = torch.bernoulli(
          self.probas.to(x.device) * torch.ones(x.shape).to(x.device)
      )
      corrupted_x = torch.where(
          should_swap == 1, x[torch.randperm(x.shape[0])], x
      )
      mask = (corrupted_x != x).float()
      return corrupted_x, mask
  
imports: import numpy
  import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the SwapNoiseCorrupter obj's underlying member method code with example usage: obj.forward(x) and returns: corrupted_x,  mask