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: Apply transformation (converting str into integers) for selected (while training) features.
docstr_tok: ["Apply","transformation","(","converting","str","into","integers",")","for","selected","(","while","training",")","features","."]
code: def transform(self, input_data: InputData) -> InputData:
      """
      Apply transformation (converting str into integers) for selected (while training) features.
      """
      copied_data = deepcopy(input_data)
      self._apply_encoder(copied_data.features)
  
      # Update features types
      feature_type_ids = copied_data.supplementary_data.col_type_ids[
          "features"
      ]
      feature_type_ids[self.binary_ids_to_convert] = TYPE_TO_ID[int]
      return copied_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 3 for calling the BinaryCategoricalPreprocessor obj's underlying member method code with example usage: obj.transform(input_data) and returns: copied_data