code: def train_step(
      state: train_state.TrainState,
      trajectories: tuple,
      batch_size: int,
      *,
      clip_param: float,
      vf_coeff: float,
      entropy_coeff: float,
  ):
      """Compilable train step.
  
      Runs an entire epoch of training (i.e. the loop over minibatches within
      an epoch is included here for performance reasons).
  
      Args:
        state: the train state
        trajectories: tuple of the following five elements forming the experience:
                      states: shape (steps_per_agent*num_agents, 84, 84, 4)
                      actions: shape (steps_per_agent*num_agents, 84, 84, 4)
                      old_log_probs: shape (steps_per_agent*num_agents, )
                      returns: shape (steps_per_agent*num_agents, )
                      advantages: (steps_per_agent*num_agents, )
        batch_size: the minibatch size, static argument
        clip_param: the PPO clipping parameter used to clamp ratios in loss function
        vf_coeff: weighs value function loss in total loss
        entropy_coeff: weighs entropy bonus in the total loss
  
      Returns:
        optimizer: new optimizer after the parameters update
        loss: loss summed over training steps
      """
      iterations = trajectories[0].shape[0] // batch_size
      trajectories = jax.tree_util.tree_map(
          lambda x: x.reshape((iterations, batch_size) + x.shape[1:]),
          trajectories,
      )
      loss = 0.0
      for batch in zip(*trajectories):
          grad_fn = jax.value_and_grad(loss_fn)
          l, grads = grad_fn(
              state.params,
              state.apply_fn,
              batch,
              clip_param,
              vf_coeff,
              entropy_coeff,
          )
          loss += l
          state = state.apply_gradients(grads=grads)
      return state, loss
  
imports: import functools
  from typing import Any, Callable
  from absl import logging
  import flax
  from flax import linen
  import agent
  import models
  import test_episodes
  from flax.metrics import tensorboard
  from flax.training import checkpoints
  from flax.training import train_state
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  import optax
label_desc: Use image node_id 3 for calling a global function with example usage: train_step(state, trajectories, batch_size) and returns: state,  loss