code: def setup_initial_state(model, tx, config, rng, mesh):
      """We initialize the model and optimizer state, and optionally load from a
      checkpoint as necessary.
  
      Args:
        model: the flax model to initialize
        tx: the optax.GradientTransformation
        config: config object
        rng: jax.prng key
        mesh: jax.devices() mesh
  
      Returns:
        state: the initialized train state
        state_mesh_annotations: the mesh annotations for the train state
      """
      init_train_state_partial = functools.partial(
          init_train_state, model, tx, config
      )
      abstract_state = jax.eval_shape(init_train_state_partial, rng)
      state_logical_annotations = nn.get_partition_spec(abstract_state)
  
      # Initialization
      with mesh, nn_partitioning.axis_rules(config.logical_axis_rules):
          state_mesh_annotations = nn.logical_to_mesh_sharding(
              state_logical_annotations, mesh, config.logical_axis_rules
          )
          state = jax.jit(
              init_train_state_partial,
              in_shardings=None,  # type: ignore
              out_shardings=state_mesh_annotations,
          )(rng)
  
      state = unbox_logicallypartioned_trainstate(state)
      return state, state_mesh_annotations
  
file_path: flax/examples/lm1b/utils.py
imports: import functools
  import logging
  import numpy
  import flax.linen
  from flax.linen import partitioning
  from flax.training import train_state
  import jax
  import jax.numpy
  from jax.experimental import mesh_utils
label_desc: Use image node_id 5 for calling a global function with example usage: setup_initial_state(model, tx, config, rng, mesh) and returns: state,  state_mesh_annotations