content: import torch
  import torch.nn as nn
  from . import SparseTensor
  from . import DEBUG
  
  __all__ = [
      'SparseGroupNorm',
      'SparseLayerNorm',
      'SparseGroupNorm32',
      'SparseLayerNorm32',
  ]
  
  
  class SparseGroupNorm(nn.GroupNorm):
      def __init__(self, num_groups, num_channels, eps=1e-5, affine=True):
          super(SparseGroupNorm, self).__init__(num_groups, num_channels, eps, affine)
  
      def forward(self, input: SparseTensor) -> SparseTensor:
          nfeats = torch.zeros_like(input.feats)
          for k in range(input.shape[0]):
              if DEBUG:
                  assert (input.coords[input.layout[k], 0] == k).all(), f"SparseGroupNorm: batch index mismatch"
              bfeats = input.feats[input.layout[k]]
              bfeats = bfeats.permute(1, 0).reshape(1, input.shape[1], -1)
              bfeats = super().forward(bfeats)
              bfeats = bfeats.reshape(input.shape[1], -1).permute(1, 0)
              nfeats[input.layout[k]] = bfeats
          return input.replace(nfeats)
  
  
  class SparseLayerNorm(nn.LayerNorm):
      def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
          super(SparseLayerNorm, self).__init__(normalized_shape, eps, elementwise_affine)
  
      def forward(self, input: SparseTensor) -> SparseTensor:
          nfeats = torch.zeros_like(input.feats)
          for k in range(input.shape[0]):
              bfeats = input.feats[input.layout[k]]
              bfeats = bfeats.permute(1, 0).reshape(1, input.shape[1], -1)
              bfeats = super().forward(bfeats)
              bfeats = bfeats.reshape(input.shape[1], -1).permute(1, 0)
              nfeats[input.layout[k]] = bfeats
          return input.replace(nfeats)
  
  
  class SparseGroupNorm32(SparseGroupNorm):
      """
      A GroupNorm layer that converts to float32 before the forward pass.
      """
      def forward(self, x: SparseTensor) -> SparseTensor:
          return super().forward(x.float()).type(x.dtype)
  
  class SparseLayerNorm32(SparseLayerNorm):
      """
      A LayerNorm layer that converts to float32 before the forward pass.
      """
      def forward(self, x: SparseTensor) -> SparseTensor:
          return super().forward(x.float()).type(x.dtype)
  