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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 from_numpy_vectors(cls, linear, quadratic, offset, vartype, variable_order=None):
 """Create a binary quadratic model from vectors. Args: linear (array_like):
 A 1D array-like iterable of linear biases. quadratic (tuple[array_like, array_like, array_like]):
 A 3-tuple of 1D array_like vectors of the form (row, col, bias). offset (numeric, optional):
 Constant offset for the binary quadratic model. vartype (:class:`.Vartype`/str/set):
 Variable type for the binary quadratic model. Accepted input values: * :class:`.Vartype.SPIN`, ``'SPIN'``, ``{-1, 1}`` * :class:`.Vartype.BINARY`, ``'BINARY'``, ``{0, 1}`` variable_order (iterable, optional):
 If provided, labels the variables; otherwise, indices are used. Returns: :obj:`.BinaryQuadraticModel` Examples: {(0, 1):
 -1.0} """


        try:
            heads, tails, values = quadratic
        except ValueError:
            raise ValueError("quadratic should be a 3-tuple")

        if not len(heads) == len(tails) == len(values):
            raise ValueError("row, col, and bias should be of equal length")

        if variable_order is None:
            variable_order = list(range(len(linear)))

        linear = {v: float(bias) for v, bias in zip(variable_order, linear)}
        quadratic = {(variable_order[u], variable_order[v]): float(bias)
                     for u, v, bias in zip(heads, tails, values)}

        return cls(linear, quadratic, offset, vartype)