code: def _make_network_factory(
      shift: Tuple[np.float64],
      scale: Tuple[np.float64],
      num_layers: int,
      num_units: int,
      dropout_rate: float,
  ) -> Callable[[specs.EnvironmentSpec], bc.BCNetworks]:
      """Returns the factory of networks to be used by the agent.
  
      Args:
        shift: Shift of the observations in demonstrations.
        scale: Scale of the observations in demonstrations.
        num_layers: Number of layers of the BC network.
        num_units: Number of units of the BC network.
        dropout_rate: Dropout rate of the BC network.
  
      Returns:
        Network factory.
      """
  
      def network_factory(spec: specs.EnvironmentSpec) -> bc.BCNetworks:
          """Creates the network used by the agent."""
  
          action_spec = spec.actions
          num_dimensions = np.prod(action_spec.shape, dtype=int)
  
          def actor_fn(obs, is_training=False, key=None):
              obs += shift
              obs *= scale
              hidden_layers = [num_units] * num_layers
              mlp = hk.Sequential(
                  [
                      hk.nets.MLP(hidden_layers + [num_dimensions]),
                  ]
              )
              if is_training:
                  return mlp(obs, dropout_rate=dropout_rate, rng=key)
              else:
                  return mlp(obs)
  
          policy = hk.without_apply_rng(hk.transform(actor_fn))
  
          # Create dummy observations to create network parameters.
          dummy_obs = utils.zeros_like(spec.observations)
          dummy_obs = utils.add_batch_dim(dummy_obs)
  
          policy_network = bc.BCPolicyNetwork(
              lambda key: policy.init(key, dummy_obs), policy.apply
          )
  
          return bc.BCNetworks(policy_network=policy_network)
  
      return network_factory
  
imports: from typing import Callable, Iterator, Tuple
  from absl import flags
  from acme import specs
  from acme import types
  from acme.agents.jax import actor_core
  from acme.agents.jax import bc
  from acme.datasets import tfds
  import helpers
  from absl import app
  from acme.jax import experiments
  from acme.jax import types
  from acme.jax import utils
  from acme.utils import lp_utils
  import dm_env
  import haiku
  import launchpad
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: _make_network_factory(shift, scale, num_layers, num_units, dropout_rate) and returns: network_factory, bc, mlp, mlp