<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def to_numpy_vectors(self, variable_order=None, dtype=np.float, index_dtype=np.int64, sort_indices=False):
 """Convert a binary quadratic model to numpy arrays. Args: variable_order (iterable, optional):
 If provided, labels the variables; otherwise, row/column indices are used. dtype (:class:`numpy.dtype`, optional):
 Data-type of the biases. By default, the data-type is inferred from the biases. index_dtype (:class:`numpy.dtype`, optional):
 Data-type of the indices. By default, the data-type is inferred from the labels. sort_indices (bool, optional, default=False):
 If True, the indices are sorted, first by row then by column. Otherwise they match :attr:`~.BinaryQuadraticModel.quadratic`. Returns: :obj:`~numpy.ndarray`: A numpy array of the linear biases. tuple: The quadratic biases in COOrdinate format. :obj:`~numpy.ndarray`: A numpy array of the row indices of the quadratic matrix entries :obj:`~numpy.ndarray`: A numpy array of the column indices of the quadratic matrix entries :obj:`~numpy.ndarray`: A numpy array of the values of the quadratic matrix entries The offset Examples: array([0., 0., 0., 0.]) array([0, 0, 2]) array([1, 3, 3]) array([ 0.5, 1.5, -1. ]) """

        linear = self.linear
        quadratic = self.quadratic

        num_variables = len(linear)
        num_interactions = len(quadratic)

        irow = np.empty(num_interactions, dtype=index_dtype)
        icol = np.empty(num_interactions, dtype=index_dtype)
        qdata = np.empty(num_interactions, dtype=dtype)

        if variable_order is None:
            try:
                ldata = np.fromiter((linear[v] for v in range(num_variables)), count=num_variables, dtype=dtype)
            except KeyError:
                raise ValueError(("if 'variable_order' is not provided, binary quadratic model must be "
                                  "index labeled [0, ..., N-1]"))

            # we could speed this up a lot with cython
            for idx, ((u, v), bias) in enumerate(quadratic.items()):
                irow[idx] = u
                icol[idx] = v
                qdata[idx] = bias

        else:
            try:
                ldata = np.fromiter((linear[v] for v in variable_order), count=num_variables, dtype=dtype)
            except KeyError:
                raise ValueError("provided 'variable_order' does not match binary quadratic model")

            label_to_idx = {v: idx for idx, v in enumerate(variable_order)}

            # we could speed this up a lot with cython
            for idx, ((u, v), bias) in enumerate(quadratic.items()):
                irow[idx] = label_to_idx[u]
                icol[idx] = label_to_idx[v]
                qdata[idx] = bias

        if sort_indices:
            # row index should be less than col index, this handles upper-triangular vs lower-triangular
            swaps = irow > icol
            if swaps.any():
                # in-place
                irow[swaps], icol[swaps] = icol[swaps], irow[swaps]

            # sort lexigraphically
            order = np.lexsort((irow, icol))
            if not (order == range(len(order))).all():
                # copy
                irow = irow[order]
                icol = icol[order]
                qdata = qdata[order]

        return ldata, (irow, icol, qdata), ldata.dtype.type(self.offset)