code: def __init__(
      self,
      num_estimator=None,
      cat_estimator=None,
      *,
      missing_values=np.nan,
      sample_posterior=False,
      max_iter=10,
      tol=1e-3,
      n_nearest_features=None,
      num_initial_strategy="mean",
      cat_initial_strategy="most_frequent",
      imputation_order="ascending",
      skip_complete=False,
      min_value=-np.inf,
      max_value=np.inf,
      verbose=0,
      random_state=None,
      add_indicator=False,
      categorical_indices=None,
      num_estimator_fit_params=None,
      cat_estimator_fit_params=None,
      num_estimator_prepare_for_categoricals_type: Optional[str] = None,
      cat_estimator_prepare_for_categoricals_type: Optional[str] = None,
  ):
      super().__init__(
          missing_values=missing_values, add_indicator=add_indicator
      )
  
      self.num_estimator = num_estimator
      self.cat_estimator = cat_estimator
      self.sample_posterior = sample_posterior
      self.max_iter = max_iter
      self.tol = tol
      self.n_nearest_features = n_nearest_features
      self.num_initial_strategy = num_initial_strategy
      self.cat_initial_strategy = cat_initial_strategy
      self.imputation_order = imputation_order
      self.skip_complete = skip_complete
      self.min_value = min_value
      self.max_value = max_value
      self.verbose = verbose
      self.random_state = random_state
      self.categorical_indices = categorical_indices
      self.num_estimator_fit_params = num_estimator_fit_params
      self.cat_estimator_fit_params = cat_estimator_fit_params
      self.num_estimator_prepare_for_categoricals_type = (
          num_estimator_prepare_for_categoricals_type
      )
      self.cat_estimator_prepare_for_categoricals_type = (
          cat_estimator_prepare_for_categoricals_type
      )
  
imports: from __future__ import annotations
  import warnings
  from time import time
  from typing import List, Optional
  import numpy
  import pandas
  from sklearn.base import BaseEstimator, clone
  from sklearn.compose import ColumnTransformer
  from sklearn.dummy import DummyClassifier
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.experimental import enable_iterative_imputer
  from sklearn.impute import IterativeImputer
  from sklearn.impute import SimpleImputer
  from sklearn.impute._iterative import FLOAT_DTYPES, _check_inputs_dtype, _get_mask, _ImputerTriplet, _safe_indexing, is_scalar_nan, stats
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import OneHotEncoder
  from sklearn.preprocessing import OrdinalEncoder
  from sklearn.utils import check_random_state
  from sklearn.utils.validation import check_is_fitted
label_desc: Use image node_id 1 to create a new IterativeImputer object from inherited base classes: SklearnIterativeImputer with example: obj = IterativeImputer(num_estimator, cat_estimator)