class_docstr: Element-wise affine transform like no-population stats BatchNorm alternative.
  
  Args:
      channels (int): Number of input tensor channels.
code: def forward(
      self, x, x_mask, reverse=False, **kwargs
  ):  # pylint: disable=unused-argument
      if not reverse:
          y = (
              x * torch.exp(self.log_scale) + self.translation
          ) * x_mask
          logdet = torch.sum(self.log_scale * x_mask, [1, 2])
          return y, logdet
      x = (x - self.translation) * torch.exp(-self.log_scale) * x_mask
      return x
  
imports: import math
  import torch
  from torch import nn
  from torch.nn import functional
  from TTS.tts.layers.generic.normalization import LayerNorm2
  from TTS.tts.layers.vits.transforms import piecewise_rational_quadratic_transform
label_desc: Use image node_id 2 for calling the ElementwiseAffine obj's underlying member method code with example usage: obj.forward(x, x_mask, reverse) and returns: x, y,  logdet