code: def setup_method(self):
      # fit for each test, because results will be changed by test
      x = self.exog
      np.random.seed(987689)
      y_count = np.random.poisson(np.exp(x.sum(1) - x.mean()))
  
      # bug with default
      model = sm.Poisson(y_count, x)
  
      # use start_params to converge faster
      start_params = np.array(
          [0.75334818, 0.99425553, 1.00494724, 1.00247112]
      )
      self.results = model.fit(
          start_params=start_params, method="bfgs", disp=0
      )
  
      # TODO: temporary, fixed in main
      self.predict_kwds = dict(exposure=1, offset=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 TestRemoveDataPicklePoisson obj's underlying member method code with example usage: obj.setup_method() without return types