def METHOD_NAME(graph, y):
    r"""Homophily measure from `Large Scale Learning on Non-Homophilous Graphs:
    New Benchmarks and Strong Simple Methods
    <https://arxiv.org/abs/2110.14446>`__
    Mathematically it is defined as follows:
    .. math::
      \frac{1}{C-1} \sum_{k=1}^{C} \max \left(0, \frac{\sum_{v\in C_k}|\{u\in
      \mathcal{N}(v): y_v = y_u \}|}{\sum_{v\in C_k}|\mathcal{N}(v)|} -
      \frac{|\mathcal{C}_k|}{|\mathcal{V}|} \right),
    where :math:`C` is the number of node classes, :math:`C_k` is the set of
    nodes that belong to class k, :math:`\mathcal{N}(v)` are the predecessors
    of node :math:`v`, :math:`y_v` is the class of node :math:`v`, and
    :math:`\mathcal{V}` is the set of nodes.
    Parameters
    ----------
    graph : DGLGraph
        The graph.
    y : torch.Tensor
        The node labels, which is a tensor of shape (|V|).
    Returns
    -------
    float
        The homophily value.
    Examples
    --------
    >>> import dgl
    >>> import torch
    >>> graph = dgl.graph(([0, 1, 2, 3], [1, 2, 0, 4]))
    >>> y = torch.tensor([0, 0, 0, 0, 1])
    >>> dgl.linkx_homophily(graph, y)
    0.19999998807907104
    """
    check_pytorch()
    with graph.local_scope():
        # Compute |{u\in N(v): y_v = y_u}| for each node v.
        # Handle the case where graph is of dtype int32.
        src, dst = get_long_edges(graph)
        # Compute y_v = y_u for all edges.
        graph.edata["same_class"] = (y[src] == y[dst]).float()
        graph.update_all(
            fn.copy_e("same_class", "m"), fn.sum("m", "same_class_deg")
        )
        deg = graph.in_degrees().float()
        num_nodes = graph.num_nodes()
        num_classes = y.max(dim=0).values.item() + 1
        value = torch.tensor(0.0).to(graph.device)
        for k in range(num_classes):
            # Get the nodes that belong to class k.
            class_mask = y == k
            same_class_deg_k = graph.ndata["same_class_deg"][class_mask].sum()
            deg_k = deg[class_mask].sum()
            num_nodes_k = class_mask.sum()
            value += max(0, same_class_deg_k / deg_k - num_nodes_k / num_nodes)
        return value.item() / (num_classes - 1)