parameters: (pos: torch.Tensor, size: torch.Tensor,
                   start: Optional[torch.Tensor] = None,
                   end: Optional[torch.Tensor] = None)
return_statement: return torch.ops.torch_cluster.grid(pos, size, start, end)
docstring: A clustering algorithm, which overlays a regular grid of user-defined
      size over a point cloud and clusters all points within a voxel.
  
      Args:
          pos (Tensor): D-dimensional position of points.
          size (Tensor): Size of a voxel in each dimension.
          start (Tensor, optional): Start position of the grid (in each
              dimension). (default: :obj:`None`)
          end (Tensor, optional): End position of the grid (in each
              dimension). (default: :obj:`None`)
  
      :rtype: :class:`LongTensor`
  
      .. code-block:: python
  
          import torch
          from torch_cluster import grid_cluster
  
          pos = torch.Tensor([[0, 0], [11, 9], [2, 8], [2, 2], [8, 3]])
          size = torch.Tensor([5, 5])
          cluster = grid_cluster(pos, size)
docstring_summary: A clustering algorithm, which overlays a regular grid of user-defined
      size over a point cloud and clusters all points within a voxel.
docstring_tokens: A clustering algorithm which overlays a regular grid of user - defined size over a point cloud and clusters all points within a voxel .
function: def grid_cluster(pos: torch.Tensor, size: torch.Tensor,
                   start: Optional[torch.Tensor] = None,
                   end: Optional[torch.Tensor] = None) -> torch.Tensor:
      """A clustering algorithm, which overlays a regular grid of user-defined
      size over a point cloud and clusters all points within a voxel.
  
      Args:
          pos (Tensor): D-dimensional position of points.
          size (Tensor): Size of a voxel in each dimension.
          start (Tensor, optional): Start position of the grid (in each
              dimension). (default: :obj:`None`)
          end (Tensor, optional): End position of the grid (in each
              dimension). (default: :obj:`None`)
  
      :rtype: :class:`LongTensor`
  
      .. code-block:: python
  
          import torch
          from torch_cluster import grid_cluster
  
          pos = torch.Tensor([[0, 0], [11, 9], [2, 8], [2, 2], [8, 3]])
          size = torch.Tensor([5, 5])
          cluster = grid_cluster(pos, size)
      """
      return torch.ops.torch_cluster.grid(pos, size, start, end)
function_tokens: def grid_cluster ( pos : torch . Tensor , size : torch . Tensor , start : Optional [ torch . Tensor ] = None , end : Optional [ torch . Tensor ] = None ) -> torch . Tensor : return torch . ops . torch_cluster . grid ( pos , size , start , end )