graspologic/pipeline/embed/adjacency_spectral_embedding.py [159:192]:
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    )

    check_argument(
        not graph.is_multigraph(),
        "Multigraphs are not supported; you must determine how to represent at most "
        "one edge between any two nodes, and handle the corresponding weights "
        "accordingly",
    )

    used_weight_attribute: Optional[str] = weight_attribute
    if not is_real_weighted(graph, weight_attribute=weight_attribute):
        warnings.warn(
            f"Graphs with edges that do not have a real numeric weight set for every "
            f"{weight_attribute} attribute on every edge are treated as an unweighted "
            f"graph - which presumes all weights are `1.0`. If this is incorrect, "
            f"please add a '{weight_attribute}' attribute to every edge with a real, "
            f"numeric value (e.g. an integer or a float) and call this function again."
        )
        used_weight_attribute = None  # this supercedes what the user said, because
        # not all of the weights are real numbers, if they exist at all
        # this weight=1.0 treatment actually happens in nx.to_scipy_sparse_matrix()

    node_labels = np.array(list(graph.nodes()))

    graph_as_csr = nx.to_scipy_sparse_matrix(
        graph, weight=used_weight_attribute, nodelist=node_labels
    )

    if not is_fully_connected(graph):
        warnings.warn("More than one connected component detected")

    graph_sans_loops = remove_loops(graph_as_csr)

    ranked_graph = pass_to_ranks(graph_sans_loops)
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graspologic/pipeline/embed/laplacian_spectral_embedding.py [175:208]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    )

    check_argument(
        not graph.is_multigraph(),
        "Multigraphs are not supported; you must determine how to represent at most "
        "one edge between any two nodes, and handle the corresponding weights "
        "accordingly",
    )

    used_weight_attribute: Optional[str] = weight_attribute
    if not is_real_weighted(graph, weight_attribute=weight_attribute):
        warnings.warn(
            f"Graphs with edges that do not have a real numeric weight set for every "
            f"{weight_attribute} attribute on every edge are treated as an unweighted "
            f"graph - which presumes all weights are `1.0`. If this is incorrect, "
            f"please add a '{weight_attribute}' attribute to every edge with a real, "
            f"numeric value (e.g. an integer or a float) and call this function again."
        )
        used_weight_attribute = None  # this supercedes what the user said, because
        # not all of the weights are real numbers, if they exist at all
        # this weight=1.0 treatment actually happens in nx.to_scipy_sparse_matrix()

    node_labels = np.array(list(graph.nodes()))

    graph_as_csr = nx.to_scipy_sparse_matrix(
        graph, weight=used_weight_attribute, nodelist=node_labels
    )

    if not is_fully_connected(graph):
        warnings.warn("More than one connected component detected")

    graph_sans_loops = remove_loops(graph_as_csr)

    ranked_graph = pass_to_ranks(graph_sans_loops)
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