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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def find_clique_embedding(k, m, n=None, t=None, target_edges=None):
 """Find an embedding for a clique in a Chimera graph. Given a target :term:`Chimera` graph size, and a clique (fully connect graph), attempts to find an embedding. Args: k (int/iterable):
 Clique to embed. If k is an integer, generates an embedding for a clique of size k labelled [0,k-1]. If k is an iterable, generates an embedding for a clique of size len(k), where iterable k is the variable labels. m (int):
 Number of rows in the Chimera lattice. n (int, optional, default=m):
 Number of columns in the Chimera lattice. t (int, optional, default 4):
 Size of the shore within each Chimera tile. target_edges (iterable[edge]):
 A list of edges in the target Chimera graph. Nodes are labelled as returned by :func:`~dwave_networkx.generators.chimera_graph`. Returns: dict: An embedding mapping a clique to the Chimera lattice. Examples: The first example finds an embedding for a :math:`K_4` complete graph in a single Chimera unit cell. The second for an alphanumerically labeled :math:`K_3` graph in 4 unit cells. {0: [4, 0], 1: [5, 1], 2: [6, 2], 3: [7, 3]} {'a': [20, 16], 'b': [21, 17], 'c': [22, 18]} """

    import random

    _, nodes = k

    m, n, t, target_edges = _chimera_input(m, n, t, target_edges)

    # Special cases to return optimal embeddings for small k.  The general clique embedder uses chains of length
    # at least 2, whereas cliques of size 1 and 2 can be embedded with single-qubit chains.

    if len(nodes) == 1:
        # If k == 1 we simply return a single chain consisting of a randomly sampled qubit.

        qubits = set().union(*target_edges)
        qubit = random.choice(tuple(qubits))
        embedding = [[qubit]]

    elif len(nodes) == 2:
        # If k == 2 we simply return two one-qubit chains that are the endpoints of a randomly sampled coupler.

        if not isinstance(target_edges, list):
            edges = list(target_edges)
        edge = edges[random.randrange(len(edges))]
        embedding = [[edge[0]], [edge[1]]]

    else:
        # General case for k > 2.

        embedding = processor(target_edges, M=m, N=n, L=t).tightestNativeClique(len(nodes))

    if not embedding:
        raise ValueError("cannot find a K{} embedding for given Chimera lattice".format(k))

    return dict(zip(nodes, embedding))