check whether problem with equality constraints is correctly constrained

import numpy as np
import hopsy

A = np.zeros((0, 2))
b = np.zeros((0,))
lower_bounds = [0, -0.5]
upper_bounds = [1, 2.0]
Aeq = np.array([[2., 1.]])
beq = np.array([1])
x0 = [0, 1]

problem = hopsy.Problem(
    A,
    b,
    None,
)
print(problem)

problem = hopsy.add_box_constraints(
    problem,
    lower_bounds,
    upper_bounds,
    simplify=False,
)
print(problem)

problem = hopsy.add_equality_constraints(
    problem,
    Aeq,
    beq
)
print(problem)

seed = 42
rng = np.random.default_rng(seed)

mc = hopsy.MarkovChain(
    problem,
    proposal=hopsy.UniformCoordinateHitAndRunProposal,
    starting_point=x0
)
rng_hopsy = hopsy.RandomNumberGenerator(seed=seed)

acceptance_rate, states = hopsy.sample(
    mc, rng_hopsy, n_samples=10000, thinning=2
)

raises

  File ~/code/CADET-Process/debug_hopsy_rosenbrock.py:38
    x0 = rosenbrock_problem.create_initial_values(10)

  File ~/code/CADET-Process/CADETProcess/optimization/optimizationProblem.py:2866 in create_initial_values
    acceptance_rate, states = hopsy.sample(

  File ~/software/miniforge3/envs/dev/lib/python3.11/site-packages/hopsy/misc.py:1220 in sample
    _accrates, _states = _sequential_sampling(

  File ~/software/miniforge3/envs/dev/lib/python3.11/site-packages/hopsy/misc.py:864 in _sequential_sampling
    accrate, state = markov_chain.draw(rng, thinning)

ValueError: UniformStepDistribution: Upper (inf) or lower limit (-inf) is unconstrained, therefore the draw is not well-defined. Try constraining the polytope with upper and lower bounds.