code: def __init__(
      self,
      data,
      perturbation_method="gaussian",
      k_pmm=20,
      history_callback=None,
  ):
      if data.columns.dtype != np.dtype("O"):
          msg = "MICEData data column names should be string type"
          raise ValueError(msg)
  
      self.regularized = dict()
  
      # Drop observations where all variables are missing.  This
      # also has the effect of copying the data frame.
      self.data = data.dropna(how="all").reset_index(drop=True)
  
      self.history_callback = history_callback
      self.history = []
      self.predict_kwds = {}
  
      # Assign the same perturbation method for all variables.
      # Can be overridden when calling 'set_imputer'.
      self.perturbation_method = defaultdict(
          lambda: perturbation_method
      )
  
      # Map from variable name to indices of observed/missing
      # values.
      self.ix_obs = {}
      self.ix_miss = {}
      for col in self.data.columns:
          ix_obs, ix_miss = self._split_indices(self.data[col])
          self.ix_obs[col] = ix_obs
          self.ix_miss[col] = ix_miss
  
      # Most recent model instance and results instance for each variable.
      self.models = {}
      self.results = {}
  
      # Map from variable names to the conditional formula.
      self.conditional_formula = {}
  
      # Map from variable names to init/fit args of the conditional
      # models.
      self.init_kwds = defaultdict(dict)
      self.fit_kwds = defaultdict(dict)
  
      # Map from variable names to the model class.
      self.model_class = {}
  
      # Map from variable names to most recent params update.
      self.params = {}
  
      # Set default imputers.
      for vname in data.columns:
          self.set_imputer(vname)
  
      # The order in which variables are imputed in each cycle.
      # Impute variables with the fewest missing values first.
      vnames = list(data.columns)
      nmiss = [len(self.ix_miss[v]) for v in vnames]
      nmiss = np.asarray(nmiss)
      ii = np.argsort(nmiss)
      ii = ii[sum(nmiss == 0) :]
      self._cycle_order = [vnames[i] for i in ii]
  
      self._initial_imputation()
  
      self.k_pmm = k_pmm
  
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 1 to create a new MICEData object  with example: obj = MICEData(data, perturbation_method, k_pmm, history_callback)