content: import os
  import sys
  
  import numpy as np
  from sklearn.utils.validation import check_is_fitted
  from sklearn.base import BaseEstimator, TransformerMixin
  
  
  class SklearnTransformerMixin(BaseEstimator, TransformerMixin):
      def fit(self, X, y=None):
          """
          Will fit the language model such that it is ready for use in scikit learn.
          Check out the [guide](https://koaning.github.io/whatlies/tutorial/languages/#scikit-learn) for more details.
          """
          if not np.array(X).dtype.type is np.str_:
              raise ValueError("You must give this preprocessor text as input.")
          self.fitted_ = True
          return self
  
      def partial_fit(self, X, y=None):
          """
          No-op.
          """
          if not np.array(X).dtype.type is np.str_:
              raise ValueError("You must give this preprocessor text as input.")
          self.fitted_ = True
          return self
  
      def transform(self, X):
          """
          Will apply the transformer as if it is a scikit-learn text feature extractor.
          Check out the [guide](https://koaning.github.io/whatlies/tutorial/languages/#scikit-learn) for more details.
          """
          check_is_fitted(self, "fitted_")
          if not np.array(X).dtype.type is np.str_:
              raise ValueError("You must give this preprocessor text as input.")
          return np.array([np.nan_to_num(self[x].vector) for x in X])
  
  
  class HiddenPrints:
      def __enter__(self):
          self._original_stdout = sys.stderr
          sys.stderr = open(os.devnull, "w")
  
      def __exit__(self, exc_type, exc_val, exc_tb):
          sys.stderr.close()
          sys.stderr = self._original_stdout
  