code: def knn_graph(
      x: Tensor,
      k: int,
      batch: OptTensor = None,
      loop: bool = False,
      flow: str = "source_to_target",
      cosine: bool = False,
      num_workers: int = 1,
      batch_size: Optional[int] = None,
  ) -> Tensor:
      r"""Computes graph edges to the nearest :obj:`k` points.
  
      .. code-block:: python
  
          import torch
          from torch_geometric.nn import knn_graph
  
          x = torch.tensor([[-1.0, -1.0], [-1.0, 1.0], [1.0, -1.0], [1.0, 1.0]])
          batch = torch.tensor([0, 0, 0, 0])
          edge_index = knn_graph(x, k=2, batch=batch, loop=False)
  
      Args:
          x (torch.Tensor): Node feature matrix
              :math:`\mathbf{X} \in \mathbb{R}^{N \times F}`.
          k (int): The number of neighbors.
          batch (torch.Tensor, optional): Batch vector
              :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
              node to a specific example. (default: :obj:`None`)
          loop (bool, optional): If :obj:`True`, the graph will contain
              self-loops. (default: :obj:`False`)
          flow (str, optional): The flow direction when using in combination with
              message passing (:obj:`"source_to_target"` or
              :obj:`"target_to_source"`). (default: :obj:`"source_to_target"`)
          cosine (bool, optional): If :obj:`True`, will use the cosine
              distance instead of euclidean distance to find nearest neighbors.
              (default: :obj:`False`)
          num_workers (int, optional): 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:`torch.Tensor`
      """
      if batch is not None and x.device != batch.device:
          warnings.warn(
              "Input tensor 'x' and 'batch' are on different devices "
              "in 'knn_graph'. Performing blocking device transfer"
          )
          batch = batch.to(x.device)
  
      if not torch_geometric.typing.WITH_TORCH_CLUSTER_BATCH_SIZE:
          return torch_cluster.knn_graph(
              x, k, batch, loop, flow, cosine, num_workers
          )
      return torch_cluster.knn_graph(
          x, k, batch, loop, flow, cosine, num_workers, batch_size
      )
  
imports: import warnings
  from typing import Optional
  from torch import Tensor
  import torch_geometric.typing
  from torch_geometric.typing import OptTensor, torch_cluster
  from .asap import ASAPooling
  from .avg_pool import avg_pool, avg_pool_neighbor_x, avg_pool_x
  from .edge_pool import EdgePooling
  from .glob import global_add_pool, global_max_pool, global_mean_pool
  from .knn import KNNIndex, L2KNNIndex, MIPSKNNIndex
  from .graclus import graclus
  from .max_pool import max_pool, max_pool_neighbor_x, max_pool_x
  from .mem_pool import MemPooling
  from .pan_pool import PANPooling
  from .sag_pool import SAGPooling
  from .topk_pool import TopKPooling
  from .voxel_grid import voxel_grid
  from .approx_knn import approx_knn, approx_knn_graph
label_desc: Use image node_id 3 for calling a global function with example usage: knn_graph(x, k, batch, loop, flow, cosine, num_workers, batch_size) and returns: torch_cluster, torch_cluster