code: def __init__(
      self, num_channels, num_groups=32, eps=1e-5, affine=True
  ):
      # NOTE num_channels is swapped to first arg for consistency in swapping norm layers with BN
      super().__init__(num_groups, num_channels, eps=eps, affine=affine)
      self.fast_norm = is_fast_norm()
  
imports: import numbers
  from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from .fast_norm import is_fast_norm, fast_group_norm, fast_layer_norm, fast_rms_norm
label_desc: Use image node_id 1 to create a new GroupNorm object from inherited base classes: nn with example: obj = GroupNorm(num_channels, num_groups, eps, affine)