class_docstr: Args:
      operation_type: ``str`` of the operation defined in operation or data operation repositories
  
          .. details:: possible operations:
  
              - ``scaling``-> ScalingImplementation,
              - ``normalization``-> NormalizationImplementation,
              - ``simple_imputation``-> ImputationImplementation,
              - ``pca``-> PCAImplementation,
              - ``kernel_pca``-> KernelPCAImplementation,
              - ``poly_features``-> PolyFeaturesImplementation,
              - ``one_hot_encoding``-> OneHotEncodingImplementation,
              - ``label_encoding``-> LabelEncodingImplementation,
              - ``fast_ica``-> FastICAImplementation
  
      params: hyperparameters to fit the operation with
class_docstr_tok: ["Args",":","operation_type",":","``","str","``","of","the","operation","defined","in","operation","or","data","operation","repositories","..","details",":",":","possible","operations",":","-","``","scaling","``","-",">","ScalingImplementation",",","-","``","normalization","``","-",">","NormalizationImplementation",",","-","``","simple_imputation","``","-",">","ImputationImplementation",",","-","``","pca","``","-",">","PCAImplementation",",","-","``","kernel_pca","``","-",">","KernelPCAImplementation",",","-","``","poly_features","``","-",">","PolyFeaturesImplementation",",","-","``","one_hot_encoding","``","-",">","OneHotEncodingImplementation",",","-","``","label_encoding","``","-",">","LabelEncodingImplementation",",","-","``","fast_ica","``","-",">","FastICAImplementation","params",":","hyperparameters","to","fit","the","operation","with"]
docstr: Transform method for preprocessing task
  
  Args:
      trained_operation: model object
      predict_data: data used for prediction
  
  Returns:
      prediction
docstr_tok: ["Transform","method","for","preprocessing","task","Args",":","trained_operation",":","model","object","predict_data",":","data","used","for","prediction","Returns",":","prediction"]
code: def predict(
      self, trained_operation, predict_data: InputData
  ) -> OutputData:
      """Transform method for preprocessing task
  
      Args:
          trained_operation: model object
          predict_data: data used for prediction
  
      Returns:
          prediction
      """
      prediction = trained_operation.transform(predict_data)
      # Convert prediction to output (if it is required)
      converted = self._convert_to_output(prediction, predict_data)
      return converted
  
file_path: FEDOT/fedot/core/operations/evaluation/common_preprocessing.py
imports: import warnings
  from typing import Optional
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.evaluation.evaluation_interfaces import EvaluationStrategy
  from fedot.core.operations.evaluation.operation_implementations.data_operations.categorical_encoders import OneHotEncodingImplementation, LabelEncodingImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_transformations import ImputationImplementation, KernelPCAImplementation, NormalizationImplementation, PCAImplementation, PolyFeaturesImplementation, ScalingImplementation, FastICAImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.topological.topological_extractor import TopologicalFeaturesImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.utilities.random import ImplementationRandomStateHandler
label_desc: Use image node_id 3 for calling the FedotPreprocessingStrategy obj's underlying member method code with example usage: obj.predict(trained_operation, predict_data) and returns: converted