class_docstr: Class for categories features preprocessing: converting binary string features into integers. 
class_docstr_tok: ["Class","for","categories","features","preprocessing",":","converting","binary","string","features","into","integers","."]
docstr: Convert labels in the column from string into int via Label encoding.
  So, Label encoder is fitted to do such transformation.
docstr_tok: ["Convert","labels","in","the","column","from","string","into","int","via","Label","encoding",".","So",",","Label","encoder","is","fitted","to","do","such","transformation","."]
code: def _train_encoder(self, column: pd.Series):
      """Convert labels in the column from string into int via Label encoding.
      So, Label encoder is fitted to do such transformation.
      """
      encoder = LabelEncoder()
      encoder.fit(column)
  
      # Store fitted label encoder for transform method
      self.binary_encoders[column.name] = encoder
  
file_path: FEDOT/fedot/preprocessing/categorical.py
imports: from copy import deepcopy
  import numpy
  import pandas
  from sklearn.preprocessing import LabelEncoder
  from fedot.core.data.data import InputData
  from fedot.core.data.data_preprocessing import find_categorical_columns
  from fedot.preprocessing.data_types import FEDOT_STR_NAN, TYPE_TO_ID
label_desc: Use image node_id 5 for calling the BinaryCategoricalPreprocessor obj's underlying member method code with example usage: obj._train_encoder(column) without return types