code: def test_many_sensitive_feature_groups_warning(
      self, transformX, transformY, transformA, A_two_dim, caplog
  ):
      # The purpose of this test case is to create enough groups to trigger certain expected
      # warnings. The scenario should still work and succeed.
      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)
  
      if A_two_dim:
          A[0][0] = 0
          A[0][1] = 0
          A[1][0] = 1
          A[1][1] = 1
          A[2][0] = 2
          A[2][1] = 2
          A[3][0] = 3
          A[3][1] = 3
          A[4][0] = 4
          A[4][1] = 4
          A[5][0] = 5
          A[5][1] = 5
      else:
          A[0] = 0
          A[1] = 1
          A[2] = 2
          A[3] = 3
          A[4] = 4
          A[5] = 5
  
      caplog.set_level(logging.WARNING)
      gs.fit(
          transformX(X), transformY(Y), sensitive_features=transformA(A)
      )
  
      log_records = caplog.get_records("call")
      dimension_log_record = log_records[0]
      size_log_record = log_records[1]
      if isinstance(self.disparity_criterion, EqualizedOdds):
          # not every label occurs with every group
          grid_dimensions = 10
      else:
          # 6 groups total, but one is not part of the basis, so 5 dimensions
          grid_dimensions = 5
  
      # expect both the dimension warning and the grid size warning
      assert len(log_records) == 2
      assert GRID_DIMENSION_WARN_TEMPLATE.format(
          grid_dimensions, GRID_DIMENSION_WARN_THRESHOLD
      ) in dimension_log_record.msg.format(*dimension_log_record.args)
      assert GRID_SIZE_WARN_TEMPLATE.format(
          grid_size, 2**grid_dimensions
      ) in size_log_record.msg.format(*size_log_record.args)
  
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 6 for calling the ArgumentTests obj's underlying member method code with example usage: obj.test_many_sensitive_feature_groups_warning(transformX, transformY, transformA, A_two_dim, caplog) without return types