code: def __init__(self):
      super().__init__()
      self.max_length: int = 200
      self.num_beams: int = 3
      self.batch_size: int = 1
      self.length_penalty: float = 1.0
      self.do_early_stopping: bool = True
      self.num_beam_hyps_to_keep: int = 1
      self.num_beam_groups: int = 1
      self.group_size: int = self.num_beams // self.num_beam_groups
      self._done = torch.zeros(self.batch_size, dtype=torch.bool)
      self._beam_hyps_count = torch.zeros(
          self.batch_size, dtype=torch.long
      )
      self._beam_hyps_worst_scores = torch.zeros(self.batch_size) + 1e9
      self._beam_hyps_max_length: int = self.max_length - 1
      self._beam_hyps: List[torch.Tensor] = [
          torch.zeros(2)
      ]  # placeholder for TorchScript compatibility
      self._beam_scores: List[torch.Tensor] = [torch.zeros(2)]
  
imports: import copy
  import itertools
  from typing import List, Optional, Tuple
  import torch
  import torch.nn.functional
  from transformers import BartConfig
  from transformers.generation import GenerationMixin
label_desc: Use image node_id 1 to create a new BeamSearchScorerTS object from inherited base classes: torch.nn with example: obj = BeamSearchScorerTS()