code: def _impute_one_feature(
      self,
      X_filled,
      mask_missing_values,
      feat_idx,
      neighbor_feat_idx,
      estimator=None,
      fit_mode=True,
  ):
      if estimator is None and fit_mode is False:
          raise ValueError(
              "If fit_mode is False, then an already-fitted "
              "estimator should be passed in."
          )
  
      is_categorical_feat = feat_idx in self.categorical_indices
  
      categorical_indices = [
          i - int(i > feat_idx)
          for i in self.categorical_indices
          if i != feat_idx
      ]
      prep_fit_params = {}
  
      if estimator is None:
          if is_categorical_feat:
              estimator = clone(self._cat_estimator)
              if self.cat_estimator_prepare_for_categoricals_type:
                  (
                      estimator,
                      prep_fit_params,
                  ) = prepare_estimator_for_categoricals(
                      estimator,
                      categorical_indices,
                      preparation_type=self.cat_estimator_prepare_for_categoricals_type,
                  )
          else:
              estimator = clone(self._num_estimator)
              if self.num_estimator_prepare_for_categoricals_type:
                  (
                      estimator,
                      prep_fit_params,
                  ) = prepare_estimator_for_categoricals(
                      estimator,
                      categorical_indices,
                      preparation_type=self.num_estimator_prepare_for_categoricals_type,
                  )
  
      missing_row_mask = mask_missing_values[:, feat_idx]
      if fit_mode:
          fit_params = (
              self.cat_estimator_fit_params
              if is_categorical_feat
              else self.num_estimator_fit_params
          )
          fit_params = fit_params or {}
          fit_params = {**fit_params, **prep_fit_params}
          X_train = _safe_indexing(
              X_filled[:, neighbor_feat_idx], ~missing_row_mask
          )
          y_train = _safe_indexing(
              X_filled[:, feat_idx], ~missing_row_mask
          )
          # required for catboost
          X_train = pd.DataFrame(X_train).astype(
              {col: int for col in categorical_indices}
          )
          if is_categorical_feat:
              y_train = y_train.astype(int)
  
          try:
              estimator.fit(X_train, y_train, **fit_params)
          except Exception:
              # some classifiers raise exceptions if there is only one
              # target value
              if is_categorical_feat and len(np.unique(y_train)) == 1:
                  estimator = DummyClassifier(strategy="most_frequent")
                  estimator.fit(X_train, y_train)
              else:
                  raise
  
      # if no missing values, don't predict
      if np.sum(missing_row_mask) == 0:
          return X_filled, estimator
  
      # get posterior samples if there is at least one missing value
      X_test = _safe_indexing(
          X_filled[:, neighbor_feat_idx], missing_row_mask
      )
      # required for catboost
      X_test = pd.DataFrame(X_test).astype(
          {col: int for col in categorical_indices}
      )
      if self.sample_posterior:
          mus, sigmas = estimator.predict(X_test, return_std=True)
          imputed_values = np.zeros(mus.shape, dtype=X_filled.dtype)
          # two types of problems: (1) non-positive sigmas
          # (2) mus outside legal range of min_value and max_value
          # (results in inf sample)
          positive_sigmas = sigmas > 0
          imputed_values[~positive_sigmas] = mus[~positive_sigmas]
          mus_too_low = mus < self._min_value[feat_idx]
          imputed_values[mus_too_low] = self._min_value[feat_idx]
          mus_too_high = mus > self._max_value[feat_idx]
          imputed_values[mus_too_high] = self._max_value[feat_idx]
          # the rest can be sampled without statistical issues
          inrange_mask = positive_sigmas & ~mus_too_low & ~mus_too_high
          mus = mus[inrange_mask]
          sigmas = sigmas[inrange_mask]
          a = (self._min_value[feat_idx] - mus) / sigmas
          b = (self._max_value[feat_idx] - mus) / sigmas
  
          truncated_normal = stats.truncnorm(
              a=a, b=b, loc=mus, scale=sigmas
          )
          imputed_values[inrange_mask] = truncated_normal.rvs(
              random_state=self.random_state_
          )
      else:
          imputed_values = estimator.predict(X_test)
          imputed_values = np.clip(
              imputed_values,
              self._min_value[feat_idx],
              self._max_value[feat_idx],
          )
  
      # update the feature
      X_filled[missing_row_mask, feat_idx] = imputed_values
      return X_filled, estimator
  
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 5 for calling the IterativeImputer obj's underlying member method code with example usage: obj._impute_one_feature(X_filled, mask_missing_values, feat_idx, neighbor_feat_idx, estimator, fit_mode) and returns: X_filled,  estimator, X_filled,  estimator