class_docstr: Gaussian dropout using multiplicative gaussian noise.
  
  https://keras.io/api/layers/regularization_layers/gaussian_dropout/
  
  Can be an effective alternative bottleneck to VAE or VQ:
  
  https://www.deepmind.com/publications/gaussian-dropout-as-an-information-bottleneck-layer
  
  Unlike some other implementations, this takes the standard deviation of the noise as input
  instead of the 'rate' typically defined as: stdev = sqrt(rate / (1 - rate))
class_docstr_tok: ["Gaussian","dropout","using","multiplicative","gaussian","noise",".","https",":","\/\/keras.io\/api\/layers\/regularization_layers\/gaussian_dropout\/","Can","be","an","effective","alternative","bottleneck","to","VAE","or","VQ",":","https",":","\/\/www.deepmind.com\/publications\/gaussian-dropout-as-an-information-bottleneck-layer","Unlike","some","other","implementations",",","this","takes","the","standard","deviation","of","the","noise","as","input","instead","of","the","'rate","'","typically","defined","as",":","stdev","=","sqrt","(","rate","\/","(","1","-","rate",")",")"]
code: def forward(self, inputs):
      if not self.training:
          return inputs
  
      noise = torch.normal(
          mean=1.0,
          std=self.stdev,
          size=inputs.shape,
          device=inputs.device,
      )
      out = noise * inputs
      return out
  
imports: from typing import Optional, Tuple
  import numpy
  import torch
  from torch import Tensor, nn
  from torch.cuda import amp
  from torch.cuda.amp import autocast
  from torch.nn import functional
  from nemo.collections.tts.modules.submodules import ConvNorm, LinearNorm, MaskedInstanceNorm1d
  from nemo.collections.tts.parts.utils.helpers import get_mask_from_lengths, sort_tensor, unsort_tensor
  from nemo.collections.tts.parts.utils.splines import piecewise_linear_inverse_transform, piecewise_linear_transform, unbounded_piecewise_quadratic_transform
label_desc: Use image node_id 2 for calling the GaussianDropout obj's underlying member method code with example usage: obj.forward(inputs) and returns: out, inputs