class_docstr: Class for categorical features encoding based on LabelEncoding 
class_docstr_tok: ["Class","for","categorical","features","encoding","based","on","LabelEncoding"]
docstr: Apply LabelEncoder on categorical features and doesn't process float or int ones
  Applicable during predict stage
docstr_tok: ["Apply","LabelEncoder","on","categorical","features","and","does","n't","process","float","or","int","ones","Applicable","during","predict","stage"]
code: def transform(self, input_data: InputData) -> OutputData:
      """Apply LabelEncoder on categorical features and doesn't process float or int ones
      Applicable during predict stage
      """
      copied_data = deepcopy(input_data)
      # If categorical features exist - transform them inplace in InputData
      self._apply_label_encoder(copied_data.features)
  
      output_data = self._convert_to_output(
          copied_data, copied_data.features
      )
  
      self._update_column_types(output_data)
      return output_data
  
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 3 for calling the LabelEncodingImplementation obj's underlying member method code with example usage: obj.transform(input_data) and returns: output_data