class_docstr: Class for automatic categorical data detection and one hot encoding 
class_docstr_tok: ["Class","for","automatic","categorical","data","detection","and","one","hot","encoding"]
docstr: The method that transforms the categorical features in the original
  dataset, but does not affect the rest features. Applicable during predict stage
  
  :param input_data: data with features, target and ids for transformation
  :return output_data: output data with transformed features table
docstr_tok: ["The","method","that","transforms","the","categorical","features","in","the","original","dataset",",","but","does","not","affect","the","rest","features",".","Applicable","during","predict","stage",":","param","input_data",":","data","with","features",",","target","and","ids","for","transformation",":","return","output_data",":","output","data","with","transformed","features","table"]
code: def transform(self, input_data: InputData) -> OutputData:
      """
      The method that transforms the categorical features in the original
      dataset, but does not affect the rest features. Applicable during predict stage
  
      :param input_data: data with features, target and ids for transformation
      :return output_data: output data with transformed features table
      """
      copied_data = deepcopy(input_data)
  
      transformed_features = copied_data.features
      if self.categorical_ids:
          # If categorical features exist
          transformed_features = self._apply_one_hot_encoding(
              transformed_features
          )
  
      # Update features
      output_data = self._convert_to_output(
          copied_data, transformed_features
      )
      self._update_column_types(output_data)
      return output_data
  
imports: from copy import deepcopy
  from typing import List, Optional
  import numpy
  from sklearn.preprocessing import LabelEncoder, OneHotEncoder
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.data.data_preprocessing import find_categorical_columns
  from fedot.core.operations.evaluation.operation_implementations.implementation_interfaces import DataOperationImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.preprocessing.data_types import TYPE_TO_ID
label_desc: Use image node_id 3 for calling the OneHotEncodingImplementation obj's underlying member method code with example usage: obj.transform(input_data) and returns: output_data