code: def __build_problem(self):
          
  
          # Set up the mathematical problem
          prob = constraint_matrices(self.model, zero_tol=self.feasibility_tol)
  
          # check if there any non-zero equality constraints
          equalities = prob.equalities
          b = prob.b
          bounds = np.atleast_2d(prob.bounds).T
          var_bounds = np.atleast_2d(prob.variable_bounds).T
          homogeneous = all(np.abs(b) < self.feasibility_tol)
          fixed_non_zero = np.abs(prob.variable_bounds[:, 1]) > \
              self.feasibility_tol
          fixed_non_zero &= prob.variable_fixed
  
          # check if there are any non-zero fixed variables, add them as
          # equalities to the stoichiometric matrix
          if any(fixed_non_zero):
              n_fixed = fixed_non_zero.sum()
              rows = np.zeros((n_fixed, prob.equalities.shape[1]))
              rows[range(n_fixed), np.where(fixed_non_zero)] = 1.0
              equalities = np.vstack([equalities, rows])
              var_b = prob.variable_bounds[:, 1]
              b = np.hstack([b, var_b[fixed_non_zero]])
              homogeneous = False
  
          # Set up a projection that can cast point into the nullspace
          nulls = nullspace(equalities)
  
          # convert bounds to a matrix and add variable bounds as well
          return Problem(
              equalities=shared_np_array(equalities.shape, equalities),
              b=shared_np_array(b.shape, b),
              inequalities=shared_np_array(prob.inequalities.shape,
                                           prob.inequalities),
              bounds=shared_np_array(bounds.shape, bounds),
              variable_fixed=shared_np_array(prob.variable_fixed.shape,
                                             prob.variable_fixed, integer=True),
              variable_bounds=shared_np_array(var_bounds.shape, var_bounds),
              nullspace=shared_np_array(nulls.shape, nulls),
              homogeneous=homogeneous
          )
docs: Build the matrix representation of the sampling problem.