class_docstr: Class for text cleaning (lemmatization and stemming) operation 
class_docstr_tok: ["Class","for","text","cleaning","(","lemmatization","and","stemming",")","operation"]
docstr: Method for transformation of the text data for predict stage
  
  :param input_data: data with features, target and ids to process
  :return output_data: output data with transformed features table
docstr_tok: ["Method","for","transformation","of","the","text","data","for","predict","stage",":","param","input_data",":","data","with","features",",","target","and","ids","to","process",":","return","output_data",":","output","data","with","transformed","features","table"]
code: def transform(self, input_data: InputData) -> OutputData:
      """Method for transformation of the text data for predict stage
  
      :param input_data: data with features, target and ids to process
      :return output_data: output data with transformed features table
      """
  
      clean_data = []
      for text in input_data.features:
          text = str(text).lower()
          words = self._word_vectorize(text)
          without_stop_words = self._remove_stop_words(words)
          words = self._lemmatization(without_stop_words)
          words = [word for word in words if word.isalpha()]
          new_text = " ".join(words)
          new_text = self._clean_html_text(new_text)
          clean_data.append(new_text)
      clean_data = np.array(clean_data)
  
      output_data = self._convert_to_output(
          input_data, clean_data, data_type=DataTypesEnum.text
      )
      return output_data
  
file_path: FEDOT/fedot/core/operations/evaluation/operation_implementations/data_operations/text_preprocessing.py
imports: import re
  from typing import Optional
  import numpy
  from golem.utilities.requirements_notificator import warn_requirement
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.core.operations.evaluation.operation_implementations.implementation_interfaces import DataOperationImplementation
  from fedot.core.repository.dataset_types import DataTypesEnum
label_desc: Use image node_id 4 for calling the TextCleanImplementation obj's underlying member method code with example usage: obj.transform(input_data) and returns: output_data