content: import torch
  import torch.nn as nn
  from .. import SparseTensor
  from .. import DEBUG
  from . import SPCONV_ALGO
  
  class SparseConv3d(nn.Module):
      def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, padding=None, bias=True, indice_key=None):
          super(SparseConv3d, self).__init__()
          if 'spconv' not in globals():
              import spconv.pytorch as spconv
          algo = None
          if SPCONV_ALGO == 'native':
              algo = spconv.ConvAlgo.Native
          elif SPCONV_ALGO == 'implicit_gemm':
              algo = spconv.ConvAlgo.MaskImplicitGemm
          if stride == 1 and (padding is None):
              self.conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, dilation=dilation, bias=bias, indice_key=indice_key, algo=algo)
          else:
              self.conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, dilation=dilation, padding=padding, bias=bias, indice_key=indice_key, algo=algo)
          self.stride = tuple(stride) if isinstance(stride, (list, tuple)) else (stride, stride, stride)
          self.padding = padding
  
      def forward(self, x: SparseTensor) -> SparseTensor:
          spatial_changed = any(s != 1 for s in self.stride) or (self.padding is not None)
          new_data = self.conv(x.data)
          new_shape = [x.shape[0], self.conv.out_channels]
          new_layout = None if spatial_changed else x.layout
  
          if spatial_changed and (x.shape[0] != 1):
              # spconv was non-1 stride will break the contiguous of the output tensor, sort by the coords
              fwd = new_data.indices[:, 0].argsort()
              bwd = torch.zeros_like(fwd).scatter_(0, fwd, torch.arange(fwd.shape[0], device=fwd.device))
              sorted_feats = new_data.features[fwd]
              sorted_coords = new_data.indices[fwd]
              unsorted_data = new_data
              new_data = spconv.SparseConvTensor(sorted_feats, sorted_coords, unsorted_data.spatial_shape, unsorted_data.batch_size)  # type: ignore
  
          out = SparseTensor(
              new_data, shape=torch.Size(new_shape), layout=new_layout,
              scale=tuple([s * stride for s, stride in zip(x._scale, self.stride)]),
              spatial_cache=x._spatial_cache,
          )
  
          if spatial_changed and (x.shape[0] != 1):
              out.register_spatial_cache(f'conv_{self.stride}_unsorted_data', unsorted_data)
              out.register_spatial_cache(f'conv_{self.stride}_sort_bwd', bwd)
   
          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 'spconv' not in globals():
              import spconv.pytorch as spconv
          self.conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, bias=bias, indice_key=indice_key)
          self.stride = tuple(stride) if isinstance(stride, (list, tuple)) else (stride, stride, stride)
  
      def forward(self, x: SparseTensor) -> SparseTensor:
          spatial_changed = any(s != 1 for s in self.stride)
          if spatial_changed:
              # recover the original spconv order
              data = x.get_spatial_cache(f'conv_{self.stride}_unsorted_data')
              bwd = x.get_spatial_cache(f'conv_{self.stride}_sort_bwd')
              data = data.replace_feature(x.feats[bwd])
              if DEBUG:
                  assert torch.equal(data.indices, x.coords[bwd]), 'Recover the original order failed'
          else:
              data = x.data
  
          new_data = self.conv(data)
          new_shape = [x.shape[0], self.conv.out_channels]
          new_layout = None if spatial_changed else x.layout
          out = SparseTensor(
              new_data, shape=torch.Size(new_shape), layout=new_layout,
              scale=tuple([s // stride for s, stride in zip(x._scale, self.stride)]),
              spatial_cache=x._spatial_cache,
          )
          return out
  