content: import torch
  import torch.nn as nn
  from .. import SparseTensor
  from trellis.backend_config import get_debug_mode, get_spconv_algo, get_sparse_backend
  
  # Get configuration from central config
  DEBUG = get_debug_mode()
  SPCONV_ALGO = get_spconv_algo()
  BACKEND = get_sparse_backend()
  
  class SparseConv3d(nn.Module):
      def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None):
          super(SparseConv3d, self).__init__()
          if BACKEND == 'torchsparse':
              import torchsparse
          self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, 0, dilation, bias)
  
      def forward(self, x: SparseTensor) -> SparseTensor:
          out = self.conv(x.data)
          new_shape = [x.shape[0], self.conv.out_channels]
          out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None)
          out._spatial_cache = x._spatial_cache
          out._scale = tuple([s * stride for s, stride in zip(x._scale, self.conv.stride)])
          return out
  
  
  class SparseInverseConv3d(nn.Module):
      def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None):
          super(SparseInverseConv3d, self).__init__()
          if BACKEND == 'torchsparse':
              import torchsparse
          self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, 0, dilation, bias, transposed=True)
  
      def forward(self, x: SparseTensor) -> SparseTensor:
          out = self.conv(x.data)        
          new_shape = [x.shape[0], self.conv.out_channels]
          out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None)
          out._spatial_cache = x._spatial_cache
          out._scale = tuple([s // stride for s, stride in zip(x._scale, self.conv.stride)])
          return out
  
  
  
  