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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 embed_qubo(source_Q, embedding, target_adjacency, chain_strength=1.0):
 """Embed a QUBO onto a target graph. Args: source_Q (dict[(variable, variable), bias]):
 Coefficients of a quadratic unconstrained binary optimization (QUBO) model. embedding (dict):
 where s is a source-model variable and t is a target-model variable. target_adjacency (dict/:class:`networkx.Graph`):
 where t is a target-graph variable and Nt is its set of neighbours. chain_strength (float, optional):
 Magnitude of the quadratic bias (in SPIN-space) applied between variables to form a chain. Note that the energy penalty of chain breaks is 2 * `chain_strength`. Returns: dict[(variable, variable), bias]: Quadratic biases of the target QUBO. Examples: This example embeds a square source graph onto fully connected :math:`K_5` graph. Embedding is accomplished by an edge deletion operation on the target graph: target-node 0 is not used. True False {(1, 1):
 -4.0, (1, 2):
 4.0, (2, 2):
 -4.0, (2, 4):
 4.0, (3, 1):
 4.0, (3, 3):
 -4.0, (4, 3):
 4.0, (4, 4):
 -4.0} This example embeds a square graph onto the target graph of a dimod reference structured sampler, `StructureComposite`, using the dimod reference `ExactSolver` sampler with a fully connected :math:`K_5` graph specified. {0: {1, 2, 3, 4}, 1: {0, 2, 3, 4}, 2: {0, 1, 3, 4}, 3: {0, 1, 2, 4}, 4: {0, 1, 2, 3}} Sample(sample={1: 0, 2: 1, 3: 1, 4: 0}, energy=-8.0) Sample(sample={1: 1, 2: 0, 3: 0, 4: 1}, energy=-8.0) Sample(sample={1: 1, 2: 0, 3: 0, 4: 0}, energy=-4.0) Sample(sample={1: 1, 2: 1, 3: 0, 4: 0}, energy=-4.0) Sample(sample={1: 0, 2: 1, 3: 0, 4: 0}, energy=-4.0) Sample(sample={1: 1, 2: 1, 3: 1, 4: 0}, energy=-4.0) """

    source_bqm = dimod.BinaryQuadraticModel.from_qubo(source_Q)
    target_bqm = embed_bqm(source_bqm, embedding, target_adjacency, chain_strength=chain_strength)
    target_Q, __ = target_bqm.to_qubo()
    return target_Q