code: def test_grid_size_warning_up_to_5_sensitive_feature_group(
      self,
      transformX,
      transformY,
      transformA,
      A_two_dim,
      n_groups,
      caplog,
  ):
      if isinstance(self.disparity_criterion, EqualizedOdds):
          pytest.skip(
              "With EqualizedOdds there would be multiple warnings due to higher grid"
              " dimensionality."
          )
  
      grid_size = 10
      gs = GridSearch(
          self.estimator,
          self.disparity_criterion,
          grid_size=grid_size,
          sample_weight_name=self.sample_weight_name,
      )
      X, Y, A = _quick_data(A_two_dim, n_groups=n_groups)
  
      caplog.set_level(logging.WARNING)
      gs.fit(
          transformX(X), transformY(Y), sensitive_features=transformA(A)
      )
  
      # don't expect the dimension warning;
      # but expect the grid size warning for large numbers of groups
      log_records = caplog.get_records("call")
  
      # 6 groups total, but one is not part of the basis, so 5 dimensions
      grid_dimensions = n_groups - 1
  
      if 2 ** (n_groups - 1) > grid_size:
          assert len(log_records) == 1
          size_log_record = log_records[0]
          assert GRID_SIZE_WARN_TEMPLATE.format(
              grid_size, 2**grid_dimensions
          ) in size_log_record.msg.format(*size_log_record.args)
      else:
          assert len(log_records) == 0
  
imports: import logging
  from test.unit.fixes import get_sklearn_expected_1d_message
  from test.unit.input_convertors import conversions_for_1d, ensure_dataframe, ensure_ndarray
  from test.unit.reductions.conftest import is_invalid_transformation
  from test.unit.reductions.grid_search.utilities import _quick_data, assert_n_grid_search_results
  import numpy
  import pandas
  import pytest
  from sklearn.exceptions import NotFittedError
  from sklearn.linear_model import LinearRegression, LogisticRegression
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  from fairlearn.reductions import BoundedGroupLoss, DemographicParity, EqualizedOdds, GridSearch, ZeroOneLoss
  from fairlearn.reductions._grid_search._grid_generator import GRID_DIMENSION_WARN_TEMPLATE, GRID_DIMENSION_WARN_THRESHOLD, GRID_SIZE_WARN_TEMPLATE, _GridGenerator
  from fairlearn.utils._input_validation import _LABELS_NOT_0_1_ERROR_MESSAGE, _MESSAGE_Y_NONE
label_desc: Use image node_id 7 for calling the ArgumentTests obj's underlying member method code with example usage: obj.test_grid_size_warning_up_to_5_sensitive_feature_group(transformX, transformY, transformA, A_two_dim, n_groups, caplog) without return types