class_docstr: Class for categorical features encoding based on LabelEncoding 
class_docstr_tok: ["Class","for","categorical","features","encoding","based","on","LabelEncoding"]
code: def fit(self, input_data: InputData):
      feature_type_ids = input_data.supplementary_data.col_type_ids[
          "features"
      ]
      (
          self.categorical_ids,
          self.non_categorical_ids,
      ) = find_categorical_columns(
          input_data.features, feature_type_ids
      )
  
      # For every existing categorical feature - perform encoding
      self._fit_label_encoders(input_data.features)
      return self.encoders
  
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 2 for calling the LabelEncodingImplementation obj's underlying member method code with example usage: obj.fit(input_data) and returns: self