code: def radius(
      x: torch.Tensor,
      y: torch.Tensor,
      r: float,
      batch_x: Optional[torch.Tensor] = None,
      batch_y: Optional[torch.Tensor] = None,
      max_num_neighbors: int = 32,
      num_workers: int = 1,
      batch_size: Optional[int] = None,
  ) -> torch.Tensor:
      r"""Finds for each element in :obj:`y` all points in :obj:`x` within
      distance :obj:`r`.
  
      Args:
          x (Tensor): Node feature matrix
              :math:`\mathbf{X} \in \mathbb{R}^{N \times F}`.
          y (Tensor): Node feature matrix
              :math:`\mathbf{Y} \in \mathbb{R}^{M \times F}`.
          r (float): The radius.
          batch_x (LongTensor, optional): Batch vector
              :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
              node to a specific example. :obj:`batch_x` needs to be sorted.
              (default: :obj:`None`)
          batch_y (LongTensor, optional): Batch vector
              :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^M`, which assigns each
              node to a specific example. :obj:`batch_y` needs to be sorted.
              (default: :obj:`None`)
          max_num_neighbors (int, optional): The maximum number of neighbors to
              return for each element in :obj:`y`.
              If the number of actual neighbors is greater than
              :obj:`max_num_neighbors`, returned neighbors are picked randomly.
              (default: :obj:`32`)
          num_workers (int): Number of workers to use for computation. Has no
              effect in case :obj:`batch_x` or :obj:`batch_y` is not
              :obj:`None`, or the input lies on the GPU. (default: :obj:`1`)
          batch_size (int, optional): The number of examples :math:`B`.
              Automatically calculated if not given. (default: :obj:`None`)
  
      .. code-block:: python
  
          import torch
          from torch_cluster import radius
  
          x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
          batch_x = torch.tensor([0, 0, 0, 0])
          y = torch.Tensor([[-1, 0], [1, 0]])
          batch_y = torch.tensor([0, 0])
          assign_index = radius(x, y, 1.5, batch_x, batch_y)
      """
      if x.numel() == 0 or y.numel() == 0:
          return torch.empty(2, 0, dtype=torch.long, device=x.device)
  
      x = x.view(-1, 1) if x.dim() == 1 else x
      y = y.view(-1, 1) if y.dim() == 1 else y
      x, y = x.contiguous(), y.contiguous()
  
      if batch_size is None:
          batch_size = 1
          if batch_x is not None:
              assert x.size(0) == batch_x.numel()
              batch_size = int(batch_x.max()) + 1
          if batch_y is not None:
              assert y.size(0) == batch_y.numel()
              batch_size = max(batch_size, int(batch_y.max()) + 1)
      assert batch_size > 0
  
      ptr_x: Optional[torch.Tensor] = None
      ptr_y: Optional[torch.Tensor] = None
  
      if batch_size > 1:
          assert batch_x is not None
          assert batch_y is not None
          arange = torch.arange(batch_size + 1, device=x.device)
          ptr_x = torch.bucketize(arange, batch_x)
          ptr_y = torch.bucketize(arange, batch_y)
  
      return torch.ops.torch_cluster.radius(
          x, y, ptr_x, ptr_y, r, max_num_neighbors, num_workers
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: radius(x, y, r, batch_x, batch_y, max_num_neighbors, num_workers, batch_size) and returns: torch, torch