class_docstr: http://jgillenw.com/cikm2018.pdf Algorithm 1
code: def unchosen_dets(self, L, chosen: List[int]):
      slate_size = L.shape[0]
      dets = torch.full((slate_size,), self.MIN_VALUE, device=L.device)
      for i in range(slate_size):
          if i not in chosen:
              dets[i] = torch.det(L[:, chosen + [i]][chosen + [i]])
      return dets
  
file_path: ReAgent/reagent/prediction/ranking/predictor_wrapper.py
imports: from enum import Enum
  from typing import List, Optional, Tuple
  import torch
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the DeterminantalPointProcessPredictorWrapper obj's underlying member method code with example usage: obj.unchosen_dets(L, chosen) and returns: dets