code: def _validate_estimators(estimators):
      """Validates estimators"""
      invalid_estimators = [
          type(estimator)
          for estimator in estimators
          if not _check_base_estimator(estimator)
      ]
      if invalid_estimators:
          raise ValueError(
              "Estimators must be sklearn estimators: got "
              "{0!r}".format(invalid_estimators)
          )
  
imports: import numbers
  import numpy
  from itertools import compress
  from scipy.sparse import issparse
  from sklearn.model_selection import ParameterGrid
  from sklearn.utils.validation import check_is_fitted
label_desc: Use image node_id 6 for calling a global function with example usage: _validate_estimators(estimators) without return types