class_docstr: Encapsulate information for computation of :class:`~gensim.models.atmodel.AuthorTopicModel`.
class_docstr_tok: ["Encapsulate","information","for","computation","of",":","class",":","`","~gensim.models.atmodel.AuthorTopicModel","`","."]
docstr: Parameters
  ----------
  eta: numpy.ndarray
      Dirichlet topic parameter for sparsity.
  lambda_shape: (int, int)
      Initialize topic parameters.
  gamma_shape: int
      Initialize topic parameters.
docstr_tok: ["Parameters","--","--","--","--","--","eta",":","numpy.ndarray","Dirichlet","topic","parameter","for","sparsity",".","lambda_shape",":","(","int",",","int",")","Initialize","topic","parameters",".","gamma_shape",":","int","Initialize","topic","parameters","."]
code: def __init__(self, eta, lambda_shape, gamma_shape):
      """
  
      Parameters
      ----------
      eta: numpy.ndarray
          Dirichlet topic parameter for sparsity.
      lambda_shape: (int, int)
          Initialize topic parameters.
      gamma_shape: int
          Initialize topic parameters.
  
      """
      self.eta = eta
      self.sstats = np.zeros(lambda_shape)
      self.gamma = np.zeros(gamma_shape)
      self.numdocs = 0
      self.dtype = np.float64
  
imports: import logging
  from itertools import chain
  from copy import deepcopy
  from shutil import copyfile
  from os.path import isfile
  from os import remove
  import numpy
  from scipy.special import gammaln
  from gensim import utils
  from gensim.models import LdaModel
  from gensim.models.ldamodel import LdaState
  from gensim.matutils import dirichlet_expectation, mean_absolute_difference
  from gensim.corpora import MmCorpus
label_desc: Use image node_id 1 to create a new AuthorTopicState object from inherited base classes: LdaState with example: obj = AuthorTopicState(eta, lambda_shape, gamma_shape)