code: def _apply_same_encoder_to_all_columns():
      """
      One shared LabelEncoder will be applied on all the data to encode it.
      """
      p = FlattenForEach(LabelEncoder(), then_unflatten=True)
  
      p, predicted_output = p.fit_transform(df.values)
  
      expected_output = np.array(
          [[6, 7, 6, 8, 7, 7], [1, 3, 0, 1, 5, 3], [4, 2, 2, 4, 4, 2]]
      ).transpose()
      assert np.array_equal(predicted_output, expected_output)
  
imports: import numpy
  import pandas
  from sklearn.preprocessing import LabelEncoder
  from neuraxle.steps.column_transformer import ColumnTransformer
  from neuraxle.steps.loop import FlattenForEach
label_desc: Use image node_id 1 for calling a global function with example usage: _apply_same_encoder_to_all_columns() without return types