code: def setUp(self):
      self.data = [0] * 9 + [1] + [0] * 10
      self.data5 = range(100)
      self.features = np.random.random((20, 2)).tolist()
      self.trend0 = trend(degree=0, discount=1.0, w=1.0)
      self.trend1 = trend(degree=0, discount=1.0)
      self.dlm1 = dlm(self.data)
      self.dlm2 = dlm(self.data)
      self.dlm3 = dlm([-1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1])
      self.dlm4 = dlm([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
      self.dlm5 = dlm(self.data5)
      self.dlm6 = dlm(self.data5)
      self.dlm1 + trend(degree=0, discount=1, w=1.0)
      self.dlm2 + trend(degree=0, discount=1e-12, w=1.0)
      self.dlm3 + seasonality(period=2, discount=1, w=1.0)
      self.dlm4 + dynamic(
          features=[[0] for i in range(5)] + [[1] for i in range(5)],
          discount=1,
          w=1.0,
      )
      (
          self.dlm5
          + trend(degree=0, discount=1, w=1.0)
          + autoReg(degree=1, discount=1, w=1.0)
      )
      (
          self.dlm6
          + trend(degree=0, discount=1, w=1.0)
          + autoReg(degree=2, discount=1, w=1.0)
      )
      self.dlm1.evolveMode("dependent")
      self.dlm2.evolveMode("dependent")
      self.dlm3.evolveMode("dependent")
      self.dlm4.evolveMode("dependent")
      self.dlm5.evolveMode("dependent")
      self.dlm6.evolveMode("dependent")
  
imports: import numpy
  import unittest
  from pydlm.modeler.trends import trend
  from pydlm.modeler.seasonality import seasonality
  from pydlm.modeler.dynamic import dynamic
  from pydlm.modeler.autoReg import autoReg
  from pydlm.dlm import dlm
label_desc: Use image node_id 1 for calling the testDlm obj's underlying member method code with example usage: obj.setUp() without return types