class_docstr: Class for categorical features encoding based on LabelEncoding 
class_docstr_tok: ["Class","for","categorical","features","encoding","based","on","LabelEncoding"]
docstr: Due to LabelEncoder has no parameters - return empty set 
docstr_tok: ["Due","to","LabelEncoder","has","no","parameters","-","return","empty","set"]
code: def get_params(self) -> OperationParameters:
      """Due to LabelEncoder has no parameters - return empty set"""
      return OperationParameters()
  
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 7 for calling the LabelEncodingImplementation obj's underlying member method code with example usage: obj.get_params() and returns: OperationParameters