code: def setup_method(self):
      # fit for each test, because results will be changed by test
      x = self.exog
      nobs = x.shape[0]
      np.random.seed(987689)
      y_bin = (
          np.random.rand(nobs) < 1.0 / (1 + np.exp(x.sum(1) - x.mean()))
      ).astype(int)
  
      # bug with default
      model = sm.Logit(y_bin, x)
  
      # use start_params to converge faster
      start_params = np.array(
          [-0.73403806, -1.00901514, -0.97754543, -0.95648212]
      )
      self.results = model.fit(
          start_params=start_params, method="bfgs", disp=0
      )
  
imports: from statsmodels.compat.pandas import assert_series_equal
  from io import BytesIO
  import pickle
  import numpy
  import pandas
  import statsmodels.api
label_desc: Use image node_id 1 for calling the TestRemoveDataPickleLogit obj's underlying member method code with example usage: obj.setup_method() without return types