docstr: Returns the next imputed dataset in the imputation process.
  
  Returns
  -------
  data : array_like
      An imputed dataset from the MICE chain.
  
  Notes
  -----
  `MICEData` does not have a `skip` parameter.  Consecutive
  values returned by `next_sample` are immediately consecutive
  in the imputation chain.
  
  The returned value is a reference to the data attribute of
  the class and should be copied before making any changes.
docstr_tok: ["Returns","the","next","imputed","dataset","in","the","imputation","process",".","Returns","--","--","--","-","data",":","array_like","An","imputed","dataset","from","the","MICE","chain",".","Notes","--","--","-","`","MICEData","`","does","not","have","a","`","skip","`","parameter",".","Consecutive","values","returned","by","`","next_sample","`","are","immediately","consecutive","in","the","imputation","chain",".","The","returned","value","is","a","reference","to","the","data","attribute","of","the","class","and","should","be","copied","before","making","any","changes","."]
code: def next_sample(self):
      """
      Returns the next imputed dataset in the imputation process.
  
      Returns
      -------
      data : array_like
          An imputed dataset from the MICE chain.
  
      Notes
      -----
      `MICEData` does not have a `skip` parameter.  Consecutive
      values returned by `next_sample` are immediately consecutive
      in the imputation chain.
  
      The returned value is a reference to the data attribute of
      the class and should be copied before making any changes.
      """
  
      self.update_all(1)
      return self.data
  
imports: import pandas
  import numpy
  import patsy
  from statsmodels.base.model import LikelihoodModelResults
  from statsmodels.regression.linear_model import OLS
  from collections import defaultdict
label_desc: Use image node_id 2 for calling the MICEData obj's underlying member method code with example usage: obj.next_sample() and returns: self