code: def __init__(self, in_channels: int):
      super().__init__(
          num_groups=16, num_channels=in_channels, eps=1e-6, affine=True
      )
  
imports: import logging
  from typing import Optional, Sequence
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from neuralcompression.layers import XCA, ChannelNorm2D
  from .._outputs._vqvae_autoencoder_output import VqVaeAutoencoderOutput
label_desc: Use image node_id 1 to create a new Normalize object from inherited base classes: torch.nn with example: obj = Normalize(in_channels)