code: def setup_method(self, method):
      self.estimator = Pipeline(
          [
              ("scaler", StandardScaler()),
              ("logistic", LogisticRegression(solver="liblinear")),
          ]
      )
      self.disparity_criterion = DemographicParity()
      self.sample_weight_name = "logistic__sample_weight"
  
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 1 for calling the TestPipelineEstimator obj's underlying member method code with example usage: obj.setup_method(method) without return types