code: def init_params(
      networks: MPONetworks,
      spec: specs.EnvironmentSpec,
      random_key: types.RNGKey,
      add_batch_dim: bool = False,
      dynamics_rollout_length: int = 0,
  ) -> Tuple[MPONetworkParams, hk.LSTMState]:
      """Initialize the parameters of a MPO network."""
  
      rng_keys = jax.random.split(random_key, 6)
  
      # Create a dummy observation/action to initialize network parameters.
      observations, actions = utils.zeros_like(
          (spec.observations, spec.actions)
      )
  
      # Add batch dimensions if necessary by the scope that is calling this init.
      if add_batch_dim:
          observations, actions = utils.add_batch_dim(
              (observations, actions)
          )
  
      # Initialize the state torso parameters and create a dummy core state.
      batch_size = 1 if add_batch_dim else None
      params_torso_initial_state = networks.torso.initial_state_fn_init(
          rng_keys[0], batch_size
      )
      state = networks.torso.initial_state_fn(
          params_torso_initial_state, batch_size
      )
  
      # Initialize the core and unroll one step to create a dummy core output.
      # The input to the core is the current action and the next observation.
      params_torso = networks.torso.init(
          rng_keys[1], observations, state
      )
      embeddings, _ = networks.torso.apply(
          params_torso, observations, state
      )
  
      # Initialize the policy and critic heads by passing in the dummy embedding.
      params_policy_head, params_critic_head = (
          {},
          {},
      )  # Cannot be None for BIT.
      if networks.policy_head:
          params_policy_head = networks.policy_head.init(
              rng_keys[2], embeddings
          )
      if networks.critic_head:
          params_critic_head = networks.critic_head.init(
              rng_keys[3], embeddings, actions
          )
  
      # Initialize the recurrent dynamics model if it exists.
      if networks.dynamics_model and dynamics_rollout_length > 0:
          params_dynamics_initial_state = (
              networks.dynamics_model.initial_state_fn_init(
                  rng_keys[4], embeddings
              )
          )
          dynamics_state = networks.dynamics_model.initial_state_fn(
              params_dynamics_initial_state, embeddings
          )
          params_dynamics = networks.dynamics_model.init(
              rng_keys[5], actions, dynamics_state
          )
      else:
          params_dynamics_initial_state = ()
          params_dynamics = ()
  
      params = MPONetworkParams(
          policy_head=params_policy_head,
          critic_head=params_critic_head,
          torso=params_torso,
          torso_initial_state=params_torso_initial_state,
          dynamics_model=params_dynamics,
          dynamics_model_initial_state=params_dynamics_initial_state,
      )
  
      return params, state
  
imports: import dataclasses
  from typing import Callable, NamedTuple, Optional, Sequence, Tuple, Union
  from acme import specs
  from acme.agents.jax.mpo import types
  from acme.jax import networks
  from acme.jax import utils
  import chex
  import haiku
  import haiku.initializers
  import jax
  import jax.numpy
  import numpy
  import tensorflow_probability.substrates.jax
label_desc: Use image node_id 1 for calling a global function with example usage: init_params(networks, spec, random_key, add_batch_dim, dynamics_rollout_length) and returns: params,  state