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 __init__(self, probas):
      super().__init__()
      self.probas = torch.from_numpy(np.array(probas))
  
imports: import numpy
  import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new SwapNoiseCorrupter object from inherited base classes: nn with example: obj = SwapNoiseCorrupter(probas)