class_docstr: Class for categorical features encoding based on LabelEncoding 
class_docstr_tok: ["Class","for","categorical","features","encoding","based","on","LabelEncoding"]
docstr: Update column types after encoding. Categorical becomes integer 
docstr_tok: ["Update","column","types","after","encoding",".","Categorical","becomes","integer"]
code: def _update_column_types(self, output_data: OutputData):
      """Update column types after encoding. Categorical becomes integer"""
      feature_type_ids = output_data.supplementary_data.col_type_ids[
          "features"
      ]
      feature_type_ids[self.categorical_ids] = TYPE_TO_ID[int]
  
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 4 for calling the LabelEncodingImplementation obj's underlying member method code with example usage: obj._update_column_types(output_data) without return types