code: def test_network_correctly_handles_stacked_rgb_frames(self):
      input_shape = (84, 84, 3, 4)  # 4 stacked RGB frames.
      example_input = jnp.zeros(input_shape, dtype=jnp.uint8)
      network_def = continuous_networks.SACEncoderNetwork()
  
      # Creating params with the example input is the first test.
      # If the input isn't handled correctly, this will throw an exception
      # and fail.
      params = network_def.init(self.key, example_input)
  
      # The first conv has a 3x3 filter, and 32 outputs. The input dimension
      # should be 3 filters x 4 stacks = 12.
      self.assertEqual(
          params["params"]["Conv_0"]["kernel"].shape, (3, 3, 12, 32)
      )
  
file_path: dopamine/tests/dopamine/labs/sac_from_pixels/continuous_networks_test.py
imports: from absl.testing import absltest
  from dopamine.labs.sac_from_pixels import continuous_networks
  import jax
  from jax import numpy
label_desc: Use image node_id 3 for calling the SACEncoderNetworkTest obj's underlying member method code with example usage: obj.test_network_correctly_handles_stacked_rgb_frames() without return types