class_docstr: Stores sufficient statistics for the current chunk of document(s) whenever Hdp model is updated with new corpus.
  These stats are used when updating lambda and top level sticks. The statistics include number of documents in the
  chunk, length of words in the documents and top level truncation level.
class_docstr_tok: ["Stores","sufficient","statistics","for","the","current","chunk","of","document","(","s",")","whenever","Hdp","model","is","updated","with","new","corpus",".","These","stats","are","used","when","updating","lambda","and","top","level","sticks",".","The","statistics","include","number","of","documents","in","the","chunk",",","length","of","words","in","the","documents","and","top","level","truncation","level","."]
docstr: Parameters
  ----------
  T : int
      Top level truncation level.
  Wt : int
      Length of words in the documents.
  Dt : int
      Chunk size.
code: def __init__(self, T, Wt, Dt):
      """
  
      Parameters
      ----------
      T : int
          Top level truncation level.
      Wt : int
          Length of words in the documents.
      Dt : int
          Chunk size.
  
      """
      self.m_chunksize = Dt
      self.m_var_sticks_ss = np.zeros(T)
      self.m_var_beta_ss = np.zeros((T, Wt))
  
imports: import logging
  import time
  import warnings
  import numpy
  from scipy.special import gammaln, psi
  from gensim import interfaces, utils, matutils
  from gensim.matutils import dirichlet_expectation, mean_absolute_difference
  from gensim.models import basemodel, ldamodel
  from gensim.utils import deprecated
label_desc: Use image node_id 1 to create a new SuffStats object  with example: obj = SuffStats(T, Wt, Dt)