class_docstr: Class for processing data without transforming encoded features.
  Encoded features - features after OneHot encoding operation, when one
  feature (with categorical values) can be represented as several boolean
  vectors
class_docstr_tok: ["Class","for","processing","data","without","transforming","encoded","features",".","Encoded","features","-","features","after","OneHot","encoding","operation",",","when","one","feature","(","with","categorical","values",")","can","be","represented","as","several","boolean","vectors"]
docstr: The method that transforms the source features using "operation" for predict stage
  
  :param input_data: tabular data with features, target and ids to process
  :return output_data: output data with transformed features table
docstr_tok: ["The","method","that","transforms","the","source","features","using","``","operation","''","for","predict","stage",":","param","input_data",":","tabular","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:
      """
      The method that transforms the source features using "operation" for predict stage
  
      :param input_data: tabular data with features, target and ids to process
      :return output_data: output data with transformed features table
      """
      source_features_shape = input_data.features.shape
      features = input_data.features
      if len(self.ids_to_process) > 0:
          transformed_features = self._make_new_table(features)
      else:
          transformed_features = features
  
      # Update features and column types
      output_data = self._convert_to_output(
          input_data, transformed_features
      )
      self._update_column_types(source_features_shape, output_data)
      return output_data
  
imports: from abc import ABC, abstractmethod
  from copy import deepcopy
  from typing import Optional
  import numpy
  from golem.core.log import default_log
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.utilities.custom_errors import AbstractMethodNotImplementError
label_desc: Use image node_id 3 for calling the EncodedInvariantImplementation obj's underlying member method code with example usage: obj.transform(input_data) and returns: output_data