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: Applies firstly :method:`fit` and then :method:`transform` methods of the class
  
  Args:
      input_data: to be trained on and transformed
  
  Returns:
      transformed ``input_data``
docstr_tok: ["Applies","firstly",":","method",":","`","fit","`","and","then",":","method",":","`","transform","`","methods","of","the","class","Args",":","input_data",":","to","be","trained","on","and","transformed","Returns",":","transformed","``","input_data","``"]
code: def fit_transform(self, input_data: InputData) -> InputData:
      """
      Applies firstly :method:`fit` and then :method:`transform` methods of the class
  
      Args:
          input_data: to be trained on and transformed
  
      Returns:
          transformed ``input_data``
      """
      self.fit(input_data)
      return self.transform(input_data)
  
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 4 for calling the BinaryCategoricalPreprocessor obj's underlying member method code with example usage: obj.fit_transform(input_data) and returns: self