class_docstr: Class for categorical features encoding based on LabelEncoding 
class_docstr_tok: ["Class","for","categorical","features","encoding","based","on","LabelEncoding"]
docstr: Applies fitted LabelEncoder for all categorical features inplace
  
  Args:
      data: numpy array with all features
docstr_tok: ["Applies","fitted","LabelEncoder","for","all","categorical","features","inplace","Args",":","data",":","numpy","array","with","all","features"]
code: def _apply_label_encoder(self, data: np.ndarray):
      """
      Applies fitted LabelEncoder for all categorical features inplace
  
      Args:
          data: numpy array with all features
      """
      categorical_columns = data[:, self.categorical_ids].astype(str)
      for column_id, column in zip(
          self.categorical_ids, categorical_columns.T
      ):
          column_encoder = self.encoders[column_id]
          column_encoder.classes_ = np.unique(
              np.concatenate((column_encoder.classes_, column))
          )
  
          transformed_column = column_encoder.transform(column)
          nan_idxs = np.flatnonzero(column == "nan")
          if len(nan_idxs):
              # Store np.nan values
              transformed_column = transformed_column.astype(object)
              transformed_column[nan_idxs] = np.nan
          data[:, column_id] = transformed_column
  
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 6 for calling the LabelEncodingImplementation obj's underlying member method code with example usage: obj._apply_label_encoder(data) without return types