code: def test_slow_update(self):
      num_outputs = 2
      # Two step sizes: 0.1, and 0.8
      kwargs = dict(
          num_outputs=num_outputs,
          scale_lb=1e-6,
          scale_ub=1e6,
      )
      initial_state, slow_update = pop_art.popart(
          step_size=1e-2, **kwargs
      )
      _, fast_update = pop_art.popart(step_size=1e-1, **kwargs)
      state = initial_state()
      params = get_constant_linear_params(num_outputs)
      targets = np.arange(6) * 3  # standard deviation > 1 and mean > 0
      indices = np.asarray([0, 0, 0, 1, 1, 1])
      _, slow_state = slow_update(params, state, targets, indices)
      _, fast_state = fast_update(params, state, targets, indices)
  
      # Faster step size means faster adjustment.
      np.testing.assert_array_less(slow_state.shift, fast_state.shift)
      np.testing.assert_array_less(slow_state.scale, fast_state.scale)
  
imports: import functools
  from absl.testing import absltest
  from absl.testing import parameterized
  import chex
  import haiku
  from haiku import data_structures
  from haiku import initializers
  import jax
  import jax.nn
  import jax.numpy
  import numpy
  from rlax._src import pop_art
label_desc: Use image node_id 5 for calling the PopArtTest obj's underlying member method code with example usage: obj.test_slow_update() without return types