class_docstr: Class for categorical features encoding based on LabelEncoding 
class_docstr_tok: ["Class","for","categorical","features","encoding","based","on","LabelEncoding"]
docstr: Fit LabelEncoder for every categorical column in the dataset 
docstr_tok: ["Fit","LabelEncoder","for","every","categorical","column","in","the","dataset"]
code: def _fit_label_encoders(self, data: np.ndarray):
      """Fit LabelEncoder for every categorical column in the dataset"""
      categorical_columns = data[:, self.categorical_ids].astype(str)
      for column_id, column in zip(
          self.categorical_ids, categorical_columns.T
      ):
          le = LabelEncoder()
          le.fit(column)
          self.encoders[column_id] = le
  
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 5 for calling the LabelEncodingImplementation obj's underlying member method code with example usage: obj._fit_label_encoders(data) without return types