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)),
        )
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])

    
python/dgl/sampling/labor.py