<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def compute_fixed_point(T, v, error_tol=1e-3, max_iter=50, verbose=2, print_skip=5, method='iteration', *args, **kwargs):
 r""" Computes and returns an approximate fixed point of the function `T`. The default method `'iteration'` simply iterates the function given an initial condition `v` and returns :math:`T^k v` when the condition :math:`\lVert T^k v - T^{k-1} v\rVert \leq \mathrm{error\_tol}` is satisfied or the number of iterations :math:`k` reaches `max_iter`. Provided that `T` is a contraction mapping or similar, :math:`T^k v` will be an approximation to the fixed point. The method `'imitation_game'` uses the "imitation game algorithm" developed by McLennan and Tourky [1]_, which internally constructs a sequence of two-player games called imitation games and utilizes their Nash equilibria, computed by the Lemke-Howson algorithm routine. It finds an approximate fixed point of `T`, a point :math:`v^*` such that :math:`\lVert T(v) - v\rVert \leq \mathrm{error\_tol}`, provided `T` is a function that satisfies the assumptions of Brouwer's fixed point theorm, i.e., a continuous function that maps a compact and convex set to itself. Parameters T : callable A callable object (e.g., function) that acts on v v : object An object such that T(v) is defined; modified in place if `method='iteration' and `v` is an array error_tol : scalar(float), optional(default=1e-3) Error tolerance max_iter : scalar(int), optional(default=50) Maximum number of iterations verbose : scalar(int), optional(default=2) Level of feedback (0 for no output, 1 for warnings only, 2 for warning and residual error reports during iteration) print_skip : scalar(int), optional(default=5) How many iterations to apply between print messages (effective only when `verbose=2`) method : str, optional(default='iteration') str in {'iteration', 'imitation_game'}. Method of computing an approximate fixed point args, kwargs : Other arguments and keyword arguments that are passed directly to the function T each time it is called Returns ------- v : object The approximate fixed point References .. [1] A. McLennan and R. Tourky, "From Imitation Games to Kakutani," 2006. """

    if max_iter < 1:
        raise ValueError('max_iter must be a positive integer')

    if verbose not in (0, 1, 2):
        raise ValueError('verbose should be 0, 1 or 2')

    if method not in ['iteration', 'imitation_game']:
        raise ValueError('invalid method')

    if method == 'imitation_game':
        is_approx_fp = \
            lambda v: _is_approx_fp(T, v, error_tol, *args, **kwargs)
        v_star, converged, iterate = \
             _compute_fixed_point_ig(T, v, max_iter, verbose, print_skip,
                                     is_approx_fp, *args, **kwargs)
        return v_star

    # method == 'iteration'
    iterate = 0

    if verbose == 2:
        start_time = time.time()
        _print_after_skip(print_skip, it=None)

    while True:
        new_v = T(v, *args, **kwargs)
        iterate += 1
        error = np.max(np.abs(new_v - v))

        try:
            v[:] = new_v
        except TypeError:
            v = new_v

        if error <= error_tol or iterate >= max_iter:
            break

        if verbose == 2:
            etime = time.time() - start_time
            _print_after_skip(print_skip, iterate, error, etime)

    if verbose == 2:
        etime = time.time() - start_time
        print_skip = 1
        _print_after_skip(print_skip, iterate, error, etime)
    if verbose >= 1:
        if error > error_tol:
            warnings.warn(_non_convergence_msg, RuntimeWarning)
        elif verbose == 2:
            print(_convergence_msg.format(iterate=iterate))

    return v