class_docstr: Class which contains abstract methods for data preprocessing.
class_docstr_tok: ["Class","which","contains","abstract","methods","for","data","preprocessing","."]
docstr: Performs obligatory preprocessing for pipeline's predict method.
  
  Args:
      data: data to be preprocessed
  
  Returns:
      preprocessed data
docstr_tok: ["Performs","obligatory","preprocessing","for","pipeline","'s","predict","method",".","Args",":","data",":","data","to","be","preprocessed","Returns",":","preprocessed","data"]
code: def obligatory_prepare_for_predict(
      self, data: Union[InputData, MultiModalData]
  ) -> Union[InputData, MultiModalData]:
      """
      Performs obligatory preprocessing for pipeline's predict method.
  
      Args:
          data: data to be preprocessed
  
      Returns:
          preprocessed data
      """
      raise AbstractMethodNotImplementError
  
imports: from abc import ABC, abstractmethod
  from typing import Dict, Union, TYPE_CHECKING
  import numpy
  from sklearn.preprocessing import LabelEncoder
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.operations.evaluation.operation_implementations.data_operations.categorical_encoders import LabelEncodingImplementation, OneHotEncodingImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_transformations import ImputationImplementation
  from fedot.preprocessing.categorical import BinaryCategoricalPreprocessor
  from fedot.preprocessing.data_types import TableTypesCorrector
  from fedot.preprocessing.structure import DEFAULT_SOURCE_NAME
  from fedot.utilities.custom_errors import AbstractMethodNotImplementError
label_desc: Use image node_id 3 for calling the BasePreprocessor obj's underlying member method code with example usage: obj.obligatory_prepare_for_predict(data) without return types