code: def __call__(self, inputs: jnp.ndarray) -> jnp.ndarray:
      mean = jnp.mean(inputs, axis=-1, keepdims=True)
      variance = jnp.var(inputs, axis=-1, keepdims=True)
  
      param_shape = inputs.shape[-1:]
      scale = hk.get_parameter(
          "scale", param_shape, inputs.dtype, init=jnp.ones
      )
      scale = jax.lax.all_gather(scale, "shard")[0]
  
      offset = hk.get_parameter(
          "offset", param_shape, inputs.dtype, init=jnp.zeros
      )
      offset = jax.lax.all_gather(offset, "shard")[0]
  
      scale = jnp.broadcast_to(scale, inputs.shape)
      offset = jnp.broadcast_to(offset, inputs.shape)
      mean = jnp.broadcast_to(mean, inputs.shape)
  
      inv = scale * jax.lax.rsqrt(variance + 1e-5)
      if self.offset:
          return inv * (inputs - mean) + offset
      else:
          return inv * (inputs - mean)
  
imports: import haiku
  import jax
  import jax.numpy
  import numpy
  from einops import rearrange, repeat
  from mesh_transformer.util import f_psum, g_psum, maybe_shard, head_print
  from jax.experimental import PartitionSpec
  from jax.experimental.maps import thread_resources
label_desc: Use image node_id 2 for calling the ReplicatedLayerNorm obj's underlying member method code with example usage: obj.__call__(inputs) and returns: unknown, unknown