class_docstr: Class which contains abstract methods for data preprocessing.
class_docstr_tok: ["Class","which","contains","abstract","methods","for","data","preprocessing","."]
docstr: Cuts large dataset based on border (number of objects to remain).
  
  Args:
      data: data to be cut
      border: number of objects to keep
docstr_tok: ["Cuts","large","dataset","based","on","border","(","number","of","objects","to","remain",")",".","Args",":","data",":","data","to","be","cut","border",":","number","of","objects","to","keep"]
code: def cut_dataset(self, data: InputData, border: int):
      """
      Cuts large dataset based on border (number of objects to remain).
  
      Args:
          data: data to be cut
          border: number of objects to keep
      """
      raise AbstractMethodNotImplementError
  
imports: from abc import ABC, abstractmethod
  from typing import Dict, Union, TYPE_CHECKING
  import numpy
  from sklearn.preprocessing import LabelEncoder
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.operations.evaluation.operation_implementations.data_operations.categorical_encoders import LabelEncodingImplementation, OneHotEncodingImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_transformations import ImputationImplementation
  from fedot.preprocessing.categorical import BinaryCategoricalPreprocessor
  from fedot.preprocessing.data_types import TableTypesCorrector
  from fedot.preprocessing.structure import DEFAULT_SOURCE_NAME
  from fedot.utilities.custom_errors import AbstractMethodNotImplementError
label_desc: Use image node_id 7 for calling the BasePreprocessor obj's underlying member method code with example usage: obj.cut_dataset(data, border) without return types