code: def _sl_fit(estimator, X, y, pb, **fit_params):
      """Aux. function to fit SlidingEstimator in parallel.
  
      Fit a clone estimator to each slice of data.
  
      Parameters
      ----------
      %(base_estimator)s
      X : array, shape (n_samples, nd_features, n_estimators)
          The target data. The feature dimension can be multidimensional e.g.
          X.shape = (n_samples, n_features_1, n_features_2, n_estimators)
      y : array, shape (n_sample, )
          The target values.
      pb : instance of ProgressBar
          The progress bar to update.
      fit_params : dict | None
          Parameters to pass to the fit method of the estimator.
  
      Returns
      -------
      estimators_ : list of estimators
          The fitted estimators.
      """
      from sklearn.base import clone
  
      estimators_ = list()
      for ii in range(X.shape[-1]):
          est = clone(estimator)
          est.fit(X[..., ii], y, **fit_params)
          estimators_.append(est)
  
          pb.update(ii + 1)
      return estimators_
  
imports: import logging
  import numpy
  from ..fixes import _get_check_scoring
  from ..parallel import parallel_func
  from ..utils import ProgressBar, _parse_verbose, array_split_idx, fill_doc, verbose
  from .base import BaseEstimator, _check_estimator
  from .mixin import TransformerMixin
label_desc: Use image node_id 1 for calling a global function with example usage: _sl_fit(estimator, X, y, pb) and returns: estimators_