code: def sample_labors(
      g,
      nodes,
      fanout,
      edge_dir="in",
      prob=None,
      importance_sampling=0,
      random_seed=None,
      seed2_contribution=0,
      copy_ndata=True,
      copy_edata=True,
      exclude_edges=None,
      output_device=None,
  ):
      """Sampler that builds computational dependency of node representations via
      labor sampling for multilayer GNN from the NeurIPS 2023 paper
      `Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs
      <https://arxiv.org/abs/2210.13339>`__
  
      This sampler will make every node gather messages from a fixed number of neighbors
      per edge type. The neighbors are picked uniformly with default parameters. For every vertex t
      that will be considered to be sampled, there will be a single random variate r_t.
  
      For each node, a number of inbound (or outbound when ``edge_dir == 'out'``) edges
      will be randomly chosen.  The graph returned will then contain all the nodes in the
      original graph, but only the sampled edges.
  
      Node/edge features are not preserved. The original IDs of
      the sampled edges are stored as the `dgl.EID` feature in the returned graph.
  
      Parameters
      ----------
      g : DGLGraph
          The graph, allowed to have multiple node or edge types. Can be either on CPU or GPU.
      nodes : tensor or dict
          Node IDs to sample neighbors from.
  
          This argument can take a single ID tensor or a dictionary of node types and ID tensors.
          If a single tensor is given, the graph must only have one type of nodes.
      fanout : int or dict[etype, int]
          The number of edges to be sampled for each node on each edge type.
  
          This argument can take a single int or a dictionary of edge types and ints.
          If a single int is given, DGL will sample this number of edges for each node for
          every edge type.
  
          If -1 is given for a single edge type, all the neighboring edges with that edge
          type will be selected.
      edge_dir : str, optional
          Determines whether to sample inbound or outbound edges.
  
          Can take either ``in`` for inbound edges or ``out`` for outbound edges.
      prob : str, optional
          Feature name used as the (unnormalized) probabilities associated with each
          neighboring edge of a node.  The feature must have only one element for each
          edge.
  
          The features must be non-negative floats, and the sum of the features of
          inbound/outbound edges for every node must be positive (though they don't have
          to sum up to one).  Otherwise, the result will be undefined.
  
          If :attr:`prob` is not None, GPU sampling is not supported.
      importance_sampling : int, optional
          Whether to use importance sampling or uniform sampling, use of negative values optimizes
          importance sampling probabilities until convergence while use of positive values runs
          optimization steps that many times. If the value is i, then LABOR-i variant is used.
      random_seed : tensor
          An int64 tensor with one element.
  
          The passed random_seed makes it so that for any seed vertex ``s`` and its neighbor ``t``,
          the rolled random variate ``r_t`` is the same for any call to this function with the same
          random seed. When sampling as part of the same batch, one would want identical seeds so that
          LABOR can globally sample. One example is that for heterogenous graphs, there is a single
          random seed passed for each edge type. This will sample much fewer vertices compared to
          having unique random seeds for each edge type. If one called this function individually for
          each edge type for a heterogenous graph with different random seeds, then it would run LABOR
          locally for each edge type, resulting into a larger number of vertices being sampled.
  
          If this function is called without a ``random_seed``, we get the random seed by getting a
          random number from DGL. Use this argument with identical random_seed if multiple calls to
          this function are used to sample as part of a single batch.
      seed2_contribution : float, optional
          A float value between [0, 1) that determines the contribution
          of the second random seed to generate the random variates for the
          LABOR sampling algorithm.
      copy_ndata: bool, optional
          If True, the node features of the new graph are copied from
          the original graph. If False, the new graph will not have any
          node features.
  
          (Default: True)
      copy_edata: bool, optional
          If True, the edge features of the new graph are copied from
          the original graph.  If False, the new graph will not have any
          edge features.
  
          (Default: True)
      exclude_edges: tensor or dict
          Edge IDs to exclude during sampling neighbors for the seed nodes.
  
          This argument can take a single ID tensor or a dictionary of edge types and ID tensors.
          If a single tensor is given, the graph must only have one type of nodes.
      output_device : Framework-specific device context object, optional
          The output device.  Default is the same as the input graph.
  
      Returns
      -------
      tuple(DGLGraph, list[Tensor])
          A sampled subgraph containing only the sampled neighboring edges along with edge weights.
  
      Notes
      -----
      If :attr:`copy_ndata` or :attr:`copy_edata` is True, same tensors are used as
      the node or edge features of the original graph and the new graph.
      As a result, users should avoid performing in-place operations
      on the node features of the new graph to avoid feature corruption.
  
      Examples
      --------
      Assume that you have the following graph
  
      >>> g = dgl.graph(([0, 0, 1, 1, 2, 2], [1, 2, 0, 1, 2, 0]))
  
      And the weights
  
      >>> g.edata['prob'] = torch.FloatTensor([0., 1., 0., 1., 0., 1.])
  
      To sample one inbound edge for node 0 and node 1:
  
      >>> sg = dgl.sampling.sample_labors(g, [0, 1], 1)
      >>> sg.edges(order='eid')
      (tensor([1, 0]), tensor([0, 1]))
      >>> sg.edata[dgl.EID]
      tensor([2, 0])
  
      To sample one inbound edge for node 0 and node 1 with probability in edge feature
      ``prob``:
  
      >>> sg = dgl.sampling.sample_labors(g, [0, 1], 1, prob='prob')
      >>> sg.edges(order='eid')
      (tensor([2, 1]), tensor([0, 1]))
  
      With ``fanout`` greater than the number of actual neighbors and without replacement,
      DGL will take all neighbors instead:
  
      >>> sg = dgl.sampling.sample_labors(g, [0, 1], 3)
      >>> sg.edges(order='eid')
      (tensor([1, 2, 0, 1]), tensor([0, 0, 1, 1]))
  
      To exclude certain EID's during sampling for the seed nodes:
  
      >>> g = dgl.graph(([0, 0, 1, 1, 2, 2], [1, 2, 0, 1, 2, 0]))
      >>> g_edges = g.all_edges(form='all')``
      (tensor([0, 0, 1, 1, 2, 2]), tensor([1, 2, 0, 1, 2, 0]), tensor([0, 1, 2, 3, 4, 5]))
      >>> sg = dgl.sampling.sample_labors(g, [0, 1], 3, exclude_edges=[0, 1, 2])
      >>> sg.all_edges(form='all')
      (tensor([2, 1]), tensor([0, 1]), tensor([0, 1]))
      >>> sg.has_edges_between(g_edges[0][:3],g_edges[1][:3])
      tensor([False, False, False])
      >>> g = dgl.heterograph({
      ...   ('drug', 'interacts', 'drug'): ([0, 0, 1, 1, 3, 2], [1, 2, 0, 1, 2, 0]),
      ...   ('drug', 'interacts', 'gene'): ([0, 0, 1, 1, 2, 2], [1, 2, 0, 1, 2, 0]),
      ...   ('drug', 'treats', 'disease'): ([0, 0, 1, 1, 2, 2], [1, 2, 0, 1, 2, 0])})
      >>> g_edges = g.all_edges(form='all', etype=('drug', 'interacts', 'drug'))
      (tensor([0, 0, 1, 1, 3, 2]), tensor([1, 2, 0, 1, 2, 0]), tensor([0, 1, 2, 3, 4, 5]))
      >>> excluded_edges  = {('drug', 'interacts', 'drug'): g_edges[2][:3]}
      >>> sg = dgl.sampling.sample_labors(g, {'drug':[0, 1]}, 3, exclude_edges=excluded_edges)
      >>> sg.all_edges(form='all', etype=('drug', 'interacts', 'drug'))
      (tensor([2, 1]), tensor([0, 1]), tensor([0, 1]))
      >>> sg.has_edges_between(g_edges[0][:3],g_edges[1][:3],etype=('drug', 'interacts', 'drug'))
      tensor([False, False, False])
  
      """
      if F.device_type(g.device) == "cpu" and not g.is_pinned():
          frontier, importances = _sample_labors(
              g,
              nodes,
              fanout,
              edge_dir=edge_dir,
              prob=prob,
              importance_sampling=importance_sampling,
              random_seed=random_seed,
              seed2_contribution=seed2_contribution,
              copy_ndata=copy_ndata,
              copy_edata=copy_edata,
              exclude_edges=exclude_edges,
          )
      else:
          frontier, importances = _sample_labors(
              g,
              nodes,
              fanout,
              edge_dir=edge_dir,
              prob=prob,
              importance_sampling=importance_sampling,
              random_seed=random_seed,
              seed2_contribution=seed2_contribution,
              copy_ndata=copy_ndata,
              copy_edata=copy_edata,
          )
          if exclude_edges is not None:
              eid_excluder = EidExcluder(exclude_edges)
              frontier, importances = eid_excluder(
                  frontier, importances
              )
      if output_device is None:
          return (frontier, importances)
      else:
          return (
              frontier.to(output_device),
              list(map(lambda x: x.to(output_device), importances)),
          )
  
file_path: dgl/python/dgl/sampling/labor.py
imports: from ..None import backend, ndarray, utils
  from .._ffi.function import _init_api
  from ..base import DGLError
  from ..heterograph import DGLGraph
  from ..random import choice
  from .utils import EidExcluder
label_desc: Use image node_id 1 for calling a global function with example usage: sample_labors(g, nodes, fanout, edge_dir, prob, importance_sampling, random_seed, seed2_contribution, copy_ndata, copy_edata, exclude_edges, output_device) and returns: frontier,  importances, frontier,  list