code: def jit_forward(forward, n_devices, do_mean=True):
      """Returns a JIT-compiled forward function running on ``n_devices``."""
      model_predict = _accelerate(forward, n_devices)
      # n_devices == 0 => CPU
      if n_devices < 2:
          return model_predict
  
      def predict(x, weights, state, rng):
          """Predict function JIT-compiled and parallelized as requested."""
          res, state = model_predict(
              reshape_by_device(x, n_devices),
              weights,
              state,
              jnp.stack(fastmath.random.split(rng, n_devices)),
          )
          res = _combine_devices(res)
          if do_mean:
              return (
                  fastmath.nested_map(
                      lambda y: mean_or_pmean(n_devices, y, axis=0), res
                  ),
                  state,
              )
          else:
              return res, state
  
      return predict
  
imports: import jax
  import numpy
  from trax import fastmath
  from trax.fastmath import numpy
  from trax.layers import base
label_desc: Use image node_id 2 for calling a global function with example usage: jit_forward(forward, n_devices, do_mean) and returns: predict, model_predict, fastmath,  state, res,  state