class_docstr: Imputed Data
  
  This class should not be instantiated directly.
  Instead, it is returned when ImputationKernel.impute_new_data() is called.
  For parameter arguments, see ImputationKernel documentation.
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docstr: Return dataset with missing values imputed.
  
  Parameters
  ----------
  dataset: int
      The dataset to complete.
  iteration: int
      Impute data with values obtained at this iteration.
      If None, returns the most up-to-date iterations,
      even if different between variables. If not none,
      iteration must have been saved in imputed values.
  inplace: bool
      Should the data be completed in place? If True,
      self.working_data is imputed,and nothing is returned.
      This is useful if the dataset is very large. If
      False, a copy of the data is returned, with missing
      values imputed.
  
  Returns
  -------
  The completed data, with values imputed for specified variables.
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code: def complete_data(
      self,
      dataset: int = 0,
      iteration: Optional[int] = None,
      inplace: bool = False,
      variables: Optional[_t_var_list] = None,
  ):
      """
      Return dataset with missing values imputed.
  
      Parameters
      ----------
      dataset: int
          The dataset to complete.
      iteration: int
          Impute data with values obtained at this iteration.
          If None, returns the most up-to-date iterations,
          even if different between variables. If not none,
          iteration must have been saved in imputed values.
      inplace: bool
          Should the data be completed in place? If True,
          self.working_data is imputed,and nothing is returned.
          This is useful if the dataset is very large. If
          False, a copy of the data is returned, with missing
          values imputed.
  
      Returns
      -------
      The completed data, with values imputed for specified variables.
  
      """
  
      # Return a copy if not inplace.
      impute_data = (
          self.working_data if inplace else self.working_data.copy()
      )
  
      # Figure out which variables we need to impute.
      # Never impute variables that are not in imputation_order.
      imp_vars = (
          self.imputation_order if variables is None else variables
      )
      imp_vars = self._get_var_ind_from_list(imp_vars)
      imp_vars = [v for v in imp_vars if v in self.imputation_order]
  
      for var in imp_vars:
          if iteration is None:
              iteration = self.iteration_count(
                  datasets=dataset, variables=var
              )
          _assign_col_values_without_copy(
              dat=impute_data,
              row_ind=self.na_where[var],
              col_ind=var,
              val=self[dataset, var, iteration],
          )
  
      if not inplace:
          return impute_data
  
imports: import numpy
  from .compat import pd_DataFrame
  from .utils import _t_dat, _t_var_list, _t_var_dict, _ensure_iterable, _dict_set_diff, _assign_col_values_without_copy, _slice, _subset_data
  from itertools import combinations
  from typing import Dict, List, Union, Any, Optional
  from warnings import warn
label_desc: Use image node_id 19 for calling the ImputedData obj's underlying member method code with example usage: obj.complete_data(dataset, iteration, inplace, variables) and returns: impute_data