class_docstr: Encapsulate information for distributed computation of :class:`~gensim.models.ldamodel.LdaModel` objects.
  
  Objects of this class are sent over the network, so try to keep them lean to
  reduce traffic.
class_docstr_tok: ["Encapsulate","information","for","distributed","computation","of",":","class",":","`","~gensim.models.ldamodel.LdaModel","`","objects",".","Objects","of","this","class","are","sent","over","the","network",",","so","try","to","keep","them","lean","to","reduce","traffic","."]
docstr: Parameters
  ----------
  eta : numpy.ndarray
      The prior probabilities assigned to each term.
  shape : tuple of (int, int)
      Shape of the sufficient statistics: (number of topics to be found, number of terms in the vocabulary).
  dtype : type
      Overrides the numpy array default types.
docstr_tok: ["Parameters","--","--","--","--","--","eta",":","numpy.ndarray","The","prior","probabilities","assigned","to","each","term",".","shape",":","tuple","of","(","int",",","int",")","Shape","of","the","sufficient","statistics",":","(","number","of","topics","to","be","found",",","number","of","terms","in","the","vocabulary",")",".","dtype",":","type","Overrides","the","numpy","array","default","types","."]
code: def __init__(self, eta, shape, dtype=np.float32):
      """
  
      Parameters
      ----------
      eta : numpy.ndarray
          The prior probabilities assigned to each term.
      shape : tuple of (int, int)
          Shape of the sufficient statistics: (number of topics to be found, number of terms in the vocabulary).
      dtype : type
          Overrides the numpy array default types.
  
      """
      self.eta = eta.astype(dtype, copy=False)
      self.sstats = np.zeros(shape, dtype=dtype)
      self.numdocs = 0
      self.dtype = dtype
  
imports: import logging
  import numbers
  import os
  import time
  from collections import defaultdict
  import numpy
  from scipy.special import gammaln, psi
  from scipy.special import polygamma
  from gensim import interfaces, utils, matutils
  from gensim.matutils import kullback_leibler, hellinger, jaccard_distance, jensen_shannon, dirichlet_expectation, logsumexp, mean_absolute_difference
  from gensim.models import basemodel, CoherenceModel
  from gensim.models.callbacks import Callback
label_desc: Use image node_id 1 to create a new LdaState object from inherited base classes: utils with example: obj = LdaState(eta, shape, dtype)