code: def knn_graph(
      x: torch.Tensor,
      k: int,
      batch: Optional[torch.Tensor] = None,
      loop: bool = False,
      flow: str = "source_to_target",
      cosine: bool = False,
      num_workers: int = 1,
      batch_size: Optional[int] = None,
  ) -> torch.Tensor:
      r"""Computes graph edges to the nearest :obj:`k` points.
  
      Args:
          x (Tensor): Node feature matrix
              :math:`\mathbf{X} \in \mathbb{R}^{N \times F}`.
          k (int): The number of neighbors.
          batch (LongTensor, optional): Batch vector
              :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
              node to a specific example. :obj:`batch` needs to be sorted.
              (default: :obj:`None`)
          loop (bool, optional): If :obj:`True`, the graph will contain
              self-loops. (default: :obj:`False`)
          flow (string, optional): The flow direction when used in combination
              with message passing (:obj:`"source_to_target"` or
              :obj:`"target_to_source"`). (default: :obj:`"source_to_target"`)
          cosine (boolean, optional): If :obj:`True`, will use the Cosine
              distance instead of Euclidean distance to find nearest neighbors.
              (default: :obj:`False`)
          num_workers (int): Number of workers to use for computation. Has no
              effect in case :obj:`batch` 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`)
  
      :rtype: :class:`LongTensor`
  
      .. code-block:: python
  
          import torch
          from torch_cluster import knn_graph
  
          x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
          batch = torch.tensor([0, 0, 0, 0])
          edge_index = knn_graph(x, k=2, batch=batch, loop=False)
      """
  
      assert flow in ["source_to_target", "target_to_source"]
      edge_index = knn(
          x,
          x,
          k if loop else k + 1,
          batch,
          batch,
          cosine,
          num_workers,
          batch_size,
      )
  
      if flow == "source_to_target":
          row, col = edge_index[1], edge_index[0]
      else:
          row, col = edge_index[0], edge_index[1]
  
      if not loop:
          mask = row != col
          row, col = row[mask], col[mask]
  
      return torch.stack([row, col], dim=0)
  
label_desc: Use image node_id 2 for calling a global function with example usage: knn_graph(x, k, batch, loop, flow, cosine, num_workers, batch_size) and returns: torch