class_docstr: Fixture generator for forecasting tests.
  
  Fixtures parameterized
  ----------------------
  estimator_class: estimator inheriting from BaseObject
      ranges over all estimator classes not excluded by EXCLUDED_TESTS
  estimator_instance: instance of estimator inheriting from BaseObject
      ranges over all estimator classes not excluded by EXCLUDED_TESTS
      instances are generated by create_test_instance class method
  scenario: instance of TestScenario
      ranges over all scenarios returned by retrieve_scenarios
class_docstr_tok: ["Fixture","generator","for","forecasting","tests",".","Fixtures","parameterized","--","--","--","--","--","--","--","--","--","--","--","estimator_class",":","estimator","inheriting","from","BaseObject","ranges","over","all","estimator","classes","not","excluded","by","EXCLUDED_TESTS","estimator_instance",":","instance","of","estimator","inheriting","from","BaseObject","ranges","over","all","estimator","classes","not","excluded","by","EXCLUDED_TESTS","instances","are","generated","by","create_test_instance","class","method","scenario",":","instance","of","TestScenario","ranges","over","all","scenarios","returned","by","retrieve_scenarios"]
docstr: Return number of columns for series generation in positive test cases.
  
  Fixtures parameterized
  ----------------------
  n_columns: int
      1 for univariate forecasters, 2 for multivariate forecasters
      ranges over 1 and 2 for forecasters which are both uni/multivariate
docstr_tok: ["Return","number","of","columns","for","series","generation","in","positive","test","cases",".","Fixtures","parameterized","--","--","--","--","--","--","--","--","--","--","--","n_columns",":","int","1","for","univariate","forecasters",",","2","for","multivariate","forecasters","ranges","over","1","and","2","for","forecasters","which","are","both","uni\/multivariate"]
code: def _generate_n_columns(self, test_name, **kwargs):
      """Return number of columns for series generation in positive test cases.
  
      Fixtures parameterized
      ----------------------
      n_columns: int
          1 for univariate forecasters, 2 for multivariate forecasters
          ranges over 1 and 2 for forecasters which are both uni/multivariate
      """
      if "estimator_class" in kwargs.keys():
          scitype_tag = kwargs["estimator_class"].get_class_tag(
              "scitype:y"
          )
      elif "estimator_instance" in kwargs.keys():
          scitype_tag = kwargs["estimator_instance"].get_tag(
              "scitype:y"
          )
      else:
          return []
  
      n_columns_list = _get_n_columns(scitype_tag)
      if len(n_columns_list) == 1:
          n_columns_names = ["" for x in n_columns_list]
      else:
          n_columns_names = [f"y:{x}cols" for x in n_columns_list]
  
      return n_columns_list, n_columns_names
  
imports: import numpy
  import pandas
  import pytest
  from sktime.datatypes import check_is_mtype
  from sktime.datatypes._utilities import get_cutoff
  from sktime.exceptions import NotFittedError
  from sktime.forecasting.base._delegate import _DelegatedForecaster
  from sktime.forecasting.base._fh import ForecastingHorizon
  from sktime.forecasting.tests._config import TEST_ALPHAS, TEST_FHS, TEST_OOS_FHS, TEST_STEP_LENGTHS_INT, TEST_WINDOW_LENGTHS_INT, VALID_INDEX_FH_COMBINATIONS
  from sktime.performance_metrics.forecasting import mean_absolute_percentage_error
  from sktime.split import ExpandingWindowSplitter, SlidingWindowSplitter, temporal_train_test_split
  from sktime.tests.test_all_estimators import BaseFixtureGenerator, QuickTester
  from sktime.utils._testing.forecasting import _assert_correct_columns, _assert_correct_pred_time_index, _get_expected_index_for_update_predict, _get_n_columns, _make_fh, make_forecasting_problem
  from sktime.utils._testing.series import _make_series
  from sktime.utils.validation.forecasting import check_fh
label_desc: Use image node_id 1 for calling the ForecasterFixtureGenerator obj's underlying member method code with example usage: obj._generate_n_columns(test_name) and returns: n_columns_list,  n_columns_names, list