code: def mapreduce(
      f: F,
      reduce_fn: Optional[Callable[[jax.Array], jax.Array]] = None,
      **vmap_kwargs,
  ) -> F:
      """A simple decorator that transforms `f` into (`reduce_fn` o vmap o f).
  
      By default, we vmap over axis 0, and the `reduce_fn` is jnp.mean over axis 0.
      Note that the call signature of `f` is invariant under this transformation.
  
      If, for example, f has shape signature [H, W] -> [N], then mapreduce(f)
      (with the default arguments) will have shape signature [B, H, W] -> [N].
  
      Args:
        f: A pure function over examples.
        reduce_fn: A pure function that reduces DeviceArrays -> DeviceArrays.
        **vmap_kwargs: Keyword arguments to forward to `jax.vmap`.
  
      Returns:
        g: A pure function over batches of examples.
      """
  
      if reduce_fn is None:
          reduce_fn = lambda x: jnp.mean(x, axis=0)
  
      vmapped_f = jax.vmap(f, **vmap_kwargs)
  
      def g(*args, **kwargs):
          return jax.tree_map(reduce_fn, vmapped_f(*args, **kwargs))
  
      return g
  
imports: import functools
  import itertools
  import queue
  import threading
  from typing import Callable, Iterable, Iterator, NamedTuple, Optional, Sequence, Tuple, TypeVar
  from absl import logging
  from acme import core
  from acme import types
  from acme.jax import types
  import haiku
  import jax
  import jax.numpy
  import numpy
  import reverb
  import tree
label_desc: Use image node_id 22 for calling a global function with example usage: mapreduce(f, reduce_fn) and returns: g, jax