class_docstr: This tests the MPPI planner to make sure it is correctly rolling out.
  
  It does not check the actual performance of the planner, as this would be a
  bit more complicated to set up.
code: def setUp(self):
      super().setUp()
      self.state_dims = 8
      self.action_dims = 4
      self.params = {
          "world": jnp.ones((3,)),
          "policy": jnp.ones((3,)),
          "value": jnp.ones((3,)),
      }
      self.env_spec = specs.EnvironmentSpec(
          observations=specs.Array(
              shape=(self.state_dims,), dtype=float
          ),
          actions=specs.Array(shape=(self.action_dims,), dtype=float),
          rewards=specs.Array(shape=(1,), dtype=float, name="reward"),
          discounts=specs.BoundedArray(
              shape=(),
              dtype=float,
              minimum=0.0,
              maximum=1.0,
              name="discount",
          ),
      )
  
imports: import functools
  from typing import Any
  from acme import specs
  from acme.agents.jax.mbop import ensemble
  from acme.agents.jax.mbop import models
  from acme.agents.jax.mbop import mppi
  from acme.jax import networks
  import jax
  import jax.numpy
  import numpy
  from absl.testing import absltest
  from absl.testing import parameterized
label_desc: Use image node_id 1 for calling the MPPITest obj's underlying member method code with example usage: obj.setUp() without return types