docstr: Args:
      :param path_to_dataset: (str) Path to the dataset to be used for evaluation.
      :param fold_size: (int) Size of the folds in cross-validation.
      :param fold_count: (int) Number of times we get a new fold, learn a model and check results as if
      there wouldn't be any offset. If there'are some offset it means that the real count of folds will
      be smaller.
      :param column_description: (str) Path to the file where column description is placed.
      :param fold_offset: (int) Number of folds we skip before begin to make cross-validation.
      :param group_column: (int) GroupId column index in the dataset file.
      'None' value means absence of grouping information in the dataset (it's the default).
      :param working_dir: Working dir for temporary files
      :param remove_models: (bool) Set true if you want models to be removed after applying them.
      :param delimiter: (str) Field delimiter used in dataset files.
      :param has_header: (bool) Set true if you want to skip first line in dataset files.
      :param partition_random_seed: (int) The seed for random value generator used for getting permutations for
       cross-validation.
      :param min_fold_count: (int) Minimun amount of folds dataset can be cut to.
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code: def __init__(
      self,
      path_to_dataset,
      fold_size,
      fold_count,
      column_description,
      fold_offset=0,
      group_column=None,
      working_dir=None,
      remove_models=True,
      delimiter="\t",
      has_header=False,
      partition_random_seed=0,
      min_fold_count=1,
  ):
      """
      Args:
          :param path_to_dataset: (str) Path to the dataset to be used for evaluation.
          :param fold_size: (int) Size of the folds in cross-validation.
          :param fold_count: (int) Number of times we get a new fold, learn a model and check results as if
          there wouldn't be any offset. If there'are some offset it means that the real count of folds will
          be smaller.
          :param column_description: (str) Path to the file where column description is placed.
          :param fold_offset: (int) Number of folds we skip before begin to make cross-validation.
          :param group_column: (int) GroupId column index in the dataset file.
          'None' value means absence of grouping information in the dataset (it's the default).
          :param working_dir: Working dir for temporary files
          :param remove_models: (bool) Set true if you want models to be removed after applying them.
          :param delimiter: (str) Field delimiter used in dataset files.
          :param has_header: (bool) Set true if you want to skip first line in dataset files.
          :param partition_random_seed: (int) The seed for random value generator used for getting permutations for
           cross-validation.
          :param min_fold_count: (int) Minimun amount of folds dataset can be cut to.
      """
      import os.path
  
      self._current_dir = os.getcwd()
      self._path_to_dataset = os.path.join(
          self._current_dir, path_to_dataset
      )
      self._column_description = (
          os.path.join(self._current_dir, column_description)
          if column_description is not None
          else None
      )
      self._fold_offset = fold_offset
      self._fold_count = fold_count
      self._fold_size = fold_size
      self._delimiter = delimiter
      self._has_header = has_header
      self._seed = partition_random_seed
      self._min_fold_count = int(min_fold_count)
      self._remove_models = remove_models
  
      if group_column is not None:
          self._group_feature_num = int(group_column)
      else:
          self._group_feature_num = group_column
  
      if working_dir is None:
          import tempfile
  
          self._working_dir = tempfile.mkdtemp()
      else:
          self._working_dir = working_dir
  
imports: from __future__ import print_function
  import os
  from copy import copy
  from enum import Enum
  from ..None import CatBoostError, CatBoost
  from .evaluation_result import EvaluationResults, MetricEvaluationResult
  from ._fold_models_handler import FoldModelsHandler
  from ._readers import _SimpleStreamingFileReader
  from ._splitter import _Splitter
  from .execution_case import ExecutionCase
  from .factor_utils import LabelMode, FactorUtils
label_desc: Use image node_id 1 to create a new CatboostEvaluation object from inherited base classes: object with example: obj = CatboostEvaluation(path_to_dataset, fold_size, fold_count, column_description, fold_offset, group_column, working_dir, remove_models, delimiter, has_header, partition_random_seed, min_fold_count)