def METHOD_NAME(graph, y):
    r"""Homophily measure from `Geom-GCN: Geometric Graph Convolutional
    Networks <https://arxiv.org/abs/2002.05287>`__
    We follow the practice of a later paper `Large Scale Learning on
    Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
    <https://arxiv.org/abs/2110.14446>`__ to call it node homophily.
    Mathematically it is defined as follows:
    .. math::
      \frac{1}{|\mathcal{V}|} \sum_{v \in \mathcal{V}} \frac{ | \{u
      \in \mathcal{N}(v): y_v = y_u \} |  } { |\mathcal{N}(v)| },
    where :math:`\mathcal{V}` is the set of nodes, :math:`\mathcal{N}(v)` is
    the predecessors of node :math:`v`, and :math:`y_v` is the class of node
    :math:`v`.
    Parameters
    ----------
    graph : DGLGraph
        The graph.
    y : torch.Tensor
        The node labels, which is a tensor of shape (|V|).
    Returns
    -------
    float
        The node homophily value.
    Examples
    --------
    >>> import dgl
    >>> import torch
    >>> graph = dgl.graph(([1, 2, 0, 4], [0, 1, 2, 3]))
    >>> y = torch.tensor([0, 0, 0, 0, 1])
    >>> dgl.node_homophily(graph, y)
    0.6000000238418579
    """
    check_pytorch()
    with graph.local_scope():
        # 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.mean("m", "same_class_deg")
        )
        return graph.ndata["same_class_deg"].mean(dim=0).item()