the
objective vector, upper bound constraints, equality constraints,
and simple bounds in a preferred format.

Parameters
----------
lp : A `scipy.optimize._linprog_util._LPProblem` consisting of the following fields:

    c : 1D array
        The coefficients of the linear objective function to be minimized.
    A_ub : 2D array, optional
        The inequality constraint matrix. Each row of ``A_ub`` specifies the
        coefficients of a linear inequality constraint on ``x``.
    b_ub : 1D array, optional
        The inequality constraint vector. Each element represents an
        upper bound on the corresponding value of ``A_ub @ x``.
    A_eq : 2D array, optional
        The equality constraint matrix. Each row of ``A_eq`` specifies the
        coefficients of a linear equality constraint on ``x``.
    b_eq : 1D array, optional
        The equality constraint vector. Each element of ``A_eq @ x`` must equal
        the corresponding element of ``b_eq``.
    bounds : various valid formats, optional
        The bounds of ``x``, as ``min`` and ``max`` pairs.
        If bounds are specified for all N variables separately, valid formats are:
        * a 2D array (2 x N or N x 2);
        * a sequence of N sequences, each with 2 values.
        If all variables have the same bounds, a single pair of values can
        be specified. Valid formats are:
        * a sequence with 2 scalar values;
        * a sequence with a single element containing 2 scalar values.
        If all variables have a lower bound of 0 and no upper bound, the bounds
        parameter can be omitted (or given as None).
    x0 : 1D array, optional
        Guess values of the decision variables, which will be refined by
        the optimization algorithm. This argument is currently used only by the
        'revised simplex' method, and can only be used if `x0` represents a
        basic feasible solution.

Returns
-------
lp : A `scipy.optimize._linprog_util._LPProblem` consisting of the following fields:

    c : 1D array
        The coefficients of the linear objective function to be minimized.
    A_ub : 2D array, optional
        The inequality constraint matrix. Each row of ``A_ub`` specifies the
        coefficients of a linear inequality constraint on ``x``.
    b_ub : 1D array, optional
        The inequality constraint vector. Each element represents an
        upper bound on the corresponding value of ``A_ub @ x``.
    A_eq : 2D array, optional
        The equality constraint matrix. Each row of ``A_eq`` specifies the
        coefficients of a linear equality constraint on ``x``.
    b_eq : 1D array, optional
        The equality constraint vector. Each element of ``A_eq @ x`` must equal
        the corresponding element of ``b_eq``.
    bounds : 2D array
        The bounds of ``x``, as ``min`` and ``max`` pairs, one for each of the N
        elements of ``x``. The N x 2 array contains lower bounds in the first
        column and upper bounds in the 2nd. Unbounded variables have lower
        bound -np.inf and/or upper bound np.inf.
    x0 : 1D array, optional
        Guess values of the decision variables, which will be refined by
        the optimization algorithm. This argument is currently used only by the
        'revised simplex' method, and can only be used if `x0` represents a
        basic feasible solution.

NTr(