code: def inverse(self, x: torch.Tensor):
      # x is assumed to be the output of PLForecastingModule.forward(), and has shape
      # (batch_size, output_chunk_length, n_targets, nr_params). we ha
      if self.affine:
          x = x - self.affine_bias.view(self.affine_bias.shape + (1,))
          x = x / (
              self.affine_weight.view(self.affine_weight.shape + (1,))
              + self.eps * self.eps
          )
      x = x * self.stdev.view(self.stdev.shape + (1,))
      x = x + self.mean.view(self.mean.shape + (1,))
      return x
  
file_path: darts/darts/models/components/layer_norm_variants.py
imports: import torch
  import torch.nn
label_desc: Use image node_id 3 for calling the RINorm obj's underlying member method code with example usage: obj.inverse(x) and returns: x