docstr: Perform one complete MICE iteration.
  
  A single MICE iteration updates all missing values using their
  respective imputation models, then fits the analysis model to
  the imputed data.
  
  Returns
  -------
  params : array_like
      The model parameters for the analysis model.
  
  Notes
  -----
  This function fits the analysis model and returns its
  parameter estimate.  The parameter vector is not stored by the
  class and is not used in any subsequent calls to `combine`.
  Use `fit` to run all MICE steps together and obtain summary
  results.
  
  The complete cycle of missing value imputation followed by
  fitting the analysis model is repeated `n_skip + 1` times and
  the analysis model parameters from the final fit are returned.
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code: def next_sample(self):
      """
      Perform one complete MICE iteration.
  
      A single MICE iteration updates all missing values using their
      respective imputation models, then fits the analysis model to
      the imputed data.
  
      Returns
      -------
      params : array_like
          The model parameters for the analysis model.
  
      Notes
      -----
      This function fits the analysis model and returns its
      parameter estimate.  The parameter vector is not stored by the
      class and is not used in any subsequent calls to `combine`.
      Use `fit` to run all MICE steps together and obtain summary
      results.
  
      The complete cycle of missing value imputation followed by
      fitting the analysis model is repeated `n_skip + 1` times and
      the analysis model parameters from the final fit are returned.
      """
  
      # Impute missing values
      self.data.update_all(self.n_skip + 1)
      start_params = None
      if len(self.results_list) > 0:
          start_params = self.results_list[-1].params
  
      # Fit the analysis model.
      model = self.model_class.from_formula(
          self.model_formula, self.data.data, **self.init_kwds
      )
      self.fit_kwds.update({"start_params": start_params})
      result = model.fit(**self.fit_kwds)
  
      return result
  
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 MICE obj's underlying member method code with example usage: obj.next_sample() and returns: result