class_docstr: http://jgillenw.com/cikm2018.pdf Algorithm 1
code: def greedy_select(self, L):
      slate_size = L.shape[0]
      dets = torch.zeros(slate_size, slate_size, device=L.device)
      chosen: List[int] = []
      unchosen = torch.ones(slate_size)
  
      if self.rerank_topk is not None:
          rerank_topk = min(self.rerank_topk, slate_size)
      else:
          rerank_topk = slate_size
  
      for i in range(rerank_topk):
          unchosen_dets = self.unchosen_dets(L, chosen)
          dets[i, :] = unchosen_dets
          chosen_idx = torch.argmax(unchosen_dets)
          chosen.append(chosen_idx.item())
          unchosen[chosen_idx] = 0
  
      final_order = torch.tensor(chosen)
      if rerank_topk != slate_size:
          final_order = torch.cat(
              (final_order, torch.nonzero(unchosen).flatten())
          )
  
      return final_order, 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 3 for calling the DeterminantalPointProcessPredictorWrapper obj's underlying member method code with example usage: obj.greedy_select(L) and returns: final_order,  dets