class_docstr: Class for categorical features encoding based on LabelEncoding 
class_docstr_tok: ["Class","for","categorical","features","encoding","based","on","LabelEncoding"]
code: def __init__(self, params: Optional[OperationParameters] = None):
      super().__init__(params)
      # LabelEncoder has no parameters
      self.encoders = {}
      self.categorical_ids: List[int] = []
      self.non_categorical_ids: List[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 1 to create a new LabelEncodingImplementation object from inherited base classes: DataOperationImplementation with example: obj = LabelEncodingImplementation(params)