code: def scale_input(
      self, y: torch.Tensor
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      # Auxiliary function to scale the observations
      scale = torch.tensor(1.0, device=y.device)
      for t in self.transforms[::-1]:
          y = t._inverse(y)
  
      for t in self.transforms:
          if isinstance(t, AffineTransform):
              scale = scale * t.scale
          else:
              scale = t(scale)
  
      return y, scale
  
imports: from typing import List, Optional, Tuple
  from cpflows.flows import DeepConvexFlow, SequentialFlow
  import torch
  from torch.distributions import AffineTransform, Distribution, Normal, TransformedDistribution
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the TransformedMQF2Distribution obj's underlying member method code with example usage: obj.scale_input(y) and returns: y,  scale