code: def setUp(self):
      super().setUp()
  
      self.r_t = jnp.array(
          [[0.0, 0.0, 1.0, 0.0, -0.5], [0.0, 0.0, 0.0, 0.0, 1.0]]
      )
      self.v_t = jnp.array(
          [
              [1.0, 4.0, -3.0, -2.0, -1.0, -1.0],
              [-3.0, -2.0, -1.0, 0.0, 5.0, -1.0],
          ]
      )
      self.discount_t = jnp.array(
          [[0.99, 0.99, 0.99, 0.99, 0.99], [0.9, 0.9, 0.9, 0.0, 0.9]]
      )
      self.dummy_rho_tm1 = jnp.array(
          [[1.0, 1.0, 1.0, 1.0, 1], [1.0, 1.0, 1.0, 1.0, 1.0]]
      )
      self.array_lambda = jnp.array(
          [[0.9, 0.9, 0.9, 0.9, 0.9], [0.9, 0.9, 0.9, 0.9, 0.9]]
      )
  
      # Different expected results for different values of lambda.
      self.expected = {}
      self.expected[1.0] = np.array(
          [
              [-1.45118, -4.4557, 2.5396, 0.5249, -0.49],
              [3.0, 2.0, 1.0, 0.0, -4.9],
          ],
          dtype=np.float32,
      )
      self.expected[0.7] = np.array(
          [
              [-0.676979, -5.248167, 2.4846, 0.6704, -0.49],
              [2.2899, 1.73, 1.0, 0.0, -4.9],
          ],
          dtype=np.float32,
      )
      self.expected[0.4] = np.array(
          [
              [0.56731, -6.042, 2.3431, 0.815, -0.49],
              [1.725, 1.46, 1.0, 0.0, -4.9],
          ],
          dtype=np.float32,
      )
  
imports: import functools
  from absl.testing import absltest
  from absl.testing import parameterized
  import chex
  import jax
  import jax.numpy
  import numpy
  from rlax._src import multistep
label_desc: Use image node_id 1 for calling the TruncatedGeneralizedAdvantageEstimationTest obj's underlying member method code with example usage: obj.setUp() without return types