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diff --git a/.venv/lib/python3.12/site-packages/networkx/generators/mycielski.py b/.venv/lib/python3.12/site-packages/networkx/generators/mycielski.py
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+"""Functions related to the Mycielski Operation and the Mycielskian family
+of graphs.
+
+"""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["mycielskian", "mycielski_graph"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(returns_graph=True)
+def mycielskian(G, iterations=1):
+    r"""Returns the Mycielskian of a simple, undirected graph G
+
+    The Mycielskian of graph preserves a graph's triangle free
+    property while increasing the chromatic number by 1.
+
+    The Mycielski Operation on a graph, :math:`G=(V, E)`, constructs a new
+    graph with :math:`2|V| + 1` nodes and :math:`3|E| + |V|` edges.
+
+    The construction is as follows:
+
+    Let :math:`V = {0, ..., n-1}`. Construct another vertex set
+    :math:`U = {n, ..., 2n}` and a vertex, `w`.
+    Construct a new graph, `M`, with vertices :math:`U \bigcup V \bigcup w`.
+    For edges, :math:`(u, v) \in E` add edges :math:`(u, v), (u, v + n)`, and
+    :math:`(u + n, v)` to M. Finally, for all vertices :math:`u \in U`, add
+    edge :math:`(u, w)` to M.
+
+    The Mycielski Operation can be done multiple times by repeating the above
+    process iteratively.
+
+    More information can be found at https://en.wikipedia.org/wiki/Mycielskian
+
+    Parameters
+    ----------
+    G : graph
+        A simple, undirected NetworkX graph
+    iterations : int
+        The number of iterations of the Mycielski operation to
+        perform on G. Defaults to 1. Must be a non-negative integer.
+
+    Returns
+    -------
+    M : graph
+        The Mycielskian of G after the specified number of iterations.
+
+    Notes
+    -----
+    Graph, node, and edge data are not necessarily propagated to the new graph.
+
+    """
+
+    M = nx.convert_node_labels_to_integers(G)
+
+    for i in range(iterations):
+        n = M.number_of_nodes()
+        M.add_nodes_from(range(n, 2 * n))
+        old_edges = list(M.edges())
+        M.add_edges_from((u, v + n) for u, v in old_edges)
+        M.add_edges_from((u + n, v) for u, v in old_edges)
+        M.add_node(2 * n)
+        M.add_edges_from((u + n, 2 * n) for u in range(n))
+
+    return M
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def mycielski_graph(n):
+    """Generator for the n_th Mycielski Graph.
+
+    The Mycielski family of graphs is an infinite set of graphs.
+    :math:`M_1` is the singleton graph, :math:`M_2` is two vertices with an
+    edge, and, for :math:`i > 2`, :math:`M_i` is the Mycielskian of
+    :math:`M_{i-1}`.
+
+    More information can be found at
+    http://mathworld.wolfram.com/MycielskiGraph.html
+
+    Parameters
+    ----------
+    n : int
+        The desired Mycielski Graph.
+
+    Returns
+    -------
+    M : graph
+        The n_th Mycielski Graph
+
+    Notes
+    -----
+    The first graph in the Mycielski sequence is the singleton graph.
+    The Mycielskian of this graph is not the :math:`P_2` graph, but rather the
+    :math:`P_2` graph with an extra, isolated vertex. The second Mycielski
+    graph is the :math:`P_2` graph, so the first two are hard coded.
+    The remaining graphs are generated using the Mycielski operation.
+
+    """
+
+    if n < 1:
+        raise nx.NetworkXError("must satisfy n >= 1")
+
+    if n == 1:
+        return nx.empty_graph(1)
+
+    else:
+        return mycielskian(nx.path_graph(2), n - 2)