code: def setUp(self):
      super().setUp()
      self._lambda = 0.9
  
      self._qs = np.array(
          [
              [[1.1, 2.1], [-1.1, 1.1], [3.1, -3.1], [-1.2, 0.0]],
              [[2.1, 3.1], [9.5, 0.1], [-2.1, -1.1], [0.1, 7.4]],
          ],
          dtype=np.float32,
      )
      self._targnet_qs = np.array(
          [
              [[1.2, 2.2], [-1.2, 0.2], [2.2, -1.2], [-2.25, -6.0]],
              [[4.2, 2.2], [1.2, 1.2], [-1.2, -2.2], [1.5, 1.0]],
          ],
          dtype=np.float32,
      )
      self._actions = np.array(
          [[0, 1, 0, 0], [1, 0, 0, 1]], dtype=np.int32
      )
      self._rewards = np.array(
          [[-1.3, -1.3, 2.3, 42.0], [1.3, 5.3, -3.3, -5.0]],
          dtype=np.float32,
      )
      self._pcontinues = np.array(
          [[0.0, 0.89, 0.85, 0.99], [0.88, 1.0, 0.83, 0.95]],
          dtype=np.float32,
      )
      self._target_policy_probs = np.array(
          [
              [[0.5, 0.5], [0.2, 0.8], [0.6, 0.4], [0.9, 0.1]],
              [[0.1, 0.9], [1.0, 0.0], [0.3, 0.7], [0.7, 0.3]],
          ],
          dtype=np.float32,
      )
      self._behavior_policy_probs = np.array(
          [[0.5, 0.1, 0.9, 0.3], [0.4, 0.6, 1.0, 0.9]], dtype=np.float32
      )
  
      self.expected_td = np.array(
          [
              [[-2.4, -2.7905, -3.0313], [0.7889, -6.3645, -0.0795]],
              [[-1.9329, -4.2626, -6.7738], [-2.3989, -9.9802, 1.4852]],
              [[-1.6179, -3.0165, -5.2699], [-2.7742, -9.9544, 2.3167]],
              [[-2.1785, -4.2530, -6.7081], [-1.3654, -8.2213, 0.7641]],
          ],
          dtype=np.float32,
      )
  
imports: import functools
  from absl.testing import absltest
  from absl.testing import parameterized
  import chex
  import jax
  import numpy
  from rlax._src import nonlinear_bellman
label_desc: Use image node_id 1 for calling the TransformedRetraceTest obj's underlying member method code with example usage: obj.setUp() without return types