code: def __init__(
      self,
      args,
      src_dict,
      tgt_dict,
      char_source_dict=None,
      char_target_dict=None,
  ):
      super().__init__(
          args,
          src_dict=src_dict,
          tgt_dict=tgt_dict,
          char_source_dict=char_source_dict,
          char_target_dict=char_target_dict,
      )
      self.top_k_probs_binary_file = args.top_k_probs_binary_file
      self.top_k_teacher_tokens = args.top_k_teacher_tokens
  
      if self.top_k_probs_binary_file is None:
          # Load model ensemble from checkpoints
          (
              self.teacher_models,
              _,
              _,
          ) = pytorch_translate_utils.load_diverse_ensemble_for_inference(
              args.teacher_path.split(":")
          )
          if torch.cuda.is_available():
              for teacher_model in self.teacher_models:
                  teacher_model = pytorch_translate_utils.maybe_cuda(
                      teacher_model
                  )
      else:
          self.teacher_models = None
  
      # Memoized scores for teacher models. By having this and gradually memoizing
      # the values, we prevent the teacher model from keeping recalculating the
      # teacher scores.
      self.top_k_teacher_scores: Dict[int, np.ndarray] = {}
      self.top_k_teacher_indices: Dict[int, np.ndarray] = {}
  
file_path: translate/pytorch_translate/tasks/knowledge_distillation_task.py
imports: from typing import Dict
  import numpy
  import torch
  from fairseq.tasks import register_task
  from pytorch_translate import constants, utils
  from pytorch_translate.data import data, utils, weighted_data
  from pytorch_translate.research.knowledge_distillation.teacher_score_data import TeacherDataset
  from pytorch_translate.tasks.pytorch_translate_task import PytorchTranslateTask
label_desc: Use image node_id 1 to create a new PytorchKnowledgeDistillationTask object from inherited base classes: PytorchTranslateTask with example: obj = PytorchKnowledgeDistillationTask(args, src_dict, tgt_dict, char_source_dict, char_target_dict)