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 creates a table based on transformed data and source boolean
  features
  
  :param features: tabular data for processing
  :return transformed_features: transformed features table
docstr_tok: ["The","method","creates","a","table","based","on","transformed","data","and","source","boolean","features",":","param","features",":","tabular","data","for","processing",":","return","transformed_features",":","transformed","features","table"]
code: def _make_new_table(self, features):
      """
      The method creates a table based on transformed data and source boolean
      features
  
      :param features: tabular data for processing
      :return transformed_features: transformed features table
      """
      features_to_process = (
          np.array(features[:, self.ids_to_process])
          if features.ndim > 1
          else features.copy()
      )
      transformed_part = self.operation.transform(features_to_process)
  
      # If there are no binary features in the dataset
      if len(self.bool_ids) == 0:
          transformed_features = transformed_part
      else:
          # Stack transformed features and bool features
          bool_features = np.array(features[:, self.bool_ids])
          frames = (bool_features, transformed_part)
          transformed_features = np.hstack(frames)
  
      return transformed_features
  
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 4 for calling the EncodedInvariantImplementation obj's underlying member method code with example usage: obj._make_new_table(features) and returns: transformed_features