class_docstr: Data object to store counts of various parameters during training.
  Includes counts for vacancies.
code: def __init__(self):
      super().__init__()
      self.head_vacancy = defaultdict(
          lambda: defaultdict(lambda: defaultdict(float))
      )
      self.head_vacancy_for_any_dv = defaultdict(
          lambda: defaultdict(float)
      )
      self.non_head_vacancy = defaultdict(
          lambda: defaultdict(lambda: defaultdict(float))
      )
      self.non_head_vacancy_for_any_dv = defaultdict(
          lambda: defaultdict(float)
      )
  
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 1 to create a new Model5Counts object from inherited base classes: Counts with example: obj = Model5Counts()