class_docstr: Latent normalization.
  
  This class applies Euclidean latent normalization along the second
  dimension prior to a subsequent child operation.
  
  Args:
      child: A child operation to follow normalization within an overall
          recursive tree.
code: def forward(
      self, output: VqVaeAutoencoderOutput
  ) -> VqVaeAutoencoderOutput:
      if output.latent is None:
          raise ValueError("Expected tensor value in latent field.")
      if self.child is not None:
          output.latent = F.normalize(output.latent, dim=1)
          return self.child(output)
      else:
          output.latent = F.normalize(output.latent, dim=1)
          return output
  
imports: from typing import Optional
  import torch.nn
  import torch.nn.functional
  from neuralcompression import VqVaeAutoencoderOutput
label_desc: Use image node_id 2 for calling the NormalizeLatent obj's underlying member method code with example usage: obj.forward(output) and returns: self, output