class_docstr: Data object to store counts of various parameters during training.
  Includes counts for vacancies.
docstr: :param count: Value to add to the vacancy counts
  :param alignment_info: Alignment under consideration
  :param i: Source word position under consideration
  :param trg_classes: Target word classes
  :param slots: Vacancy states of the slots in the target sentence.
      Output parameter that will be modified as new words are placed
      in the target sentence.
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code: def update_vacancy(
      self, count, alignment_info, i, trg_classes, slots
  ):
      """
      :param count: Value to add to the vacancy counts
      :param alignment_info: Alignment under consideration
      :param i: Source word position under consideration
      :param trg_classes: Target word classes
      :param slots: Vacancy states of the slots in the target sentence.
          Output parameter that will be modified as new words are placed
          in the target sentence.
      """
      tablet = alignment_info.cepts[i]
      tablet_length = len(tablet)
      total_vacancies = slots.vacancies_at(len(slots))
  
      # case 1: NULL aligned words
      if tablet_length == 0:
          return  # ignore zero fertility words
  
      # case 2: head word
      j = tablet[0]
      previous_cept = alignment_info.previous_cept(j)
      previous_center = alignment_info.center_of_cept(previous_cept)
      dv = slots.vacancies_at(j) - slots.vacancies_at(previous_center)
      max_v = total_vacancies - tablet_length + 1
      trg_class = trg_classes[alignment_info.trg_sentence[j]]
      self.head_vacancy[dv][max_v][trg_class] += count
      self.head_vacancy_for_any_dv[max_v][trg_class] += count
      slots.occupy(j)  # mark position as occupied
      total_vacancies -= 1
  
      # case 3: non-head words
      for k in range(1, tablet_length):
          previous_position = tablet[k - 1]
          previous_vacancies = slots.vacancies_at(previous_position)
          j = tablet[k]
          dv = slots.vacancies_at(j) - previous_vacancies
          max_v = (
              total_vacancies
              - tablet_length
              + k
              + 1
              - previous_vacancies
          )
          trg_class = trg_classes[alignment_info.trg_sentence[j]]
          self.non_head_vacancy[dv][max_v][trg_class] += count
          self.non_head_vacancy_for_any_dv[max_v][trg_class] += count
          slots.occupy(j)  # mark position as occupied
          total_vacancies -= 1
  
imports: import warnings
  from collections import defaultdict
  from math import factorial
  from nltk.translate import AlignedSent, Alignment, IBMModel, IBMModel4
  from nltk.translate.ibm_model import Counts, longest_target_sentence_length
label_desc: Use image node_id 2 for calling the Model5Counts obj's underlying member method code with example usage: obj.update_vacancy(count, alignment_info, i, trg_classes, slots) without return types