code: def for_n_devices(x, n_devices):
      """Replicates/broadcasts ``x`` for ``n_devices``."""
  
      def f(x):
          if n_devices > 1 and fastmath.is_backend(
              fastmath.Backend.JAX
          ):
              return jax.device_put_replicated(x, jax.local_devices())
          elif n_devices > 1:
              return jnp.broadcast_to(
                  x, (n_devices,) + jnp.asarray(x).shape
              )
          else:
              return x
  
      return fastmath.nested_map(f, x)
  
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 6 for calling a global function with example usage: for_n_devices(x, n_devices) and returns: fastmath, jax, jnp, x