class_docstr: Class for automatic categorical data detection and one hot encoding 
class_docstr_tok: ["Class","for","automatic","categorical","data","detection","and","one","hot","encoding"]
docstr: The method creates a table based on categorical and real features after One Hot Encoding transformation
  
  :param features: tabular data for processing
  :return transformed_features: transformed features table
docstr_tok: ["The","method","creates","a","table","based","on","categorical","and","real","features","after","One","Hot","Encoding","transformation",":","param","features",":","tabular","data","for","processing",":","return","transformed_features",":","transformed","features","table"]
code: def _apply_one_hot_encoding(self, features: np.ndarray) -> np.ndarray:
      """
      The method creates a table based on categorical and real features after One Hot Encoding transformation
  
      :param features: tabular data for processing
      :return transformed_features: transformed features table
      """
      transformed_categorical = self.encoder.transform(
          features[:, self.categorical_ids]
      ).toarray()
  
      # Stack transformed categorical and non-categorical data, ignore if none
      non_categorical_features = features[:, self.non_categorical_ids]
      frames = (non_categorical_features, transformed_categorical)
      transformed_features = np.hstack(frames)
      self.encoded_ids = np.array(
          range(
              non_categorical_features.shape[1],
              transformed_features.shape[1],
          )
      )
  
      return transformed_features
  
imports: from copy import deepcopy
  from typing import List, Optional
  import numpy
  from sklearn.preprocessing import LabelEncoder, OneHotEncoder
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.data.data_preprocessing import find_categorical_columns
  from fedot.core.operations.evaluation.operation_implementations.implementation_interfaces import DataOperationImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.preprocessing.data_types import TYPE_TO_ID
label_desc: Use image node_id 5 for calling the OneHotEncodingImplementation obj's underlying member method code with example usage: obj._apply_one_hot_encoding(features) and returns: transformed_features