code: def init_state(
      rng: jnp.ndarray,
      config: ml_collections.ConfigDict,
      model: Any,
      dataset: dataset_utils.Dataset,
      workdir: str,
  ):
      """Initialize the model state."""
      rng, init_rng = jax.random.split(rng)
      (
          params,
          model_state,
          num_trainable_params,
          gflops,
      ) = train_utils.initialize_model(
          model_def=model.flax_model,
          input_spec=[
              (
                  dataset.meta_data["input_shape"],
                  dataset.meta_data.get("input_dtype", jnp.float32),
              )
          ],
          config=config,
          rngs=init_rng,
      )
  
      # Create optimizer.
      lr_fn = lr_schedules.get_learning_rate_fn(config)
      optimizer_config = optimizers.get_optax_optimizer_config(config)
      # If the config is already an optax-compatible config, better call directly:
      #   optimizers.get_optimizer(config.optimizer_configs, lr_fn)
      tx = optimizers.get_optimizer(
          optimizer_config, lr_fn, params=params
      )
      # We jit this, such that the arrays that are created are created on the same
      # device as the input is, in this case the CPU. Else they'd be on device[0].
      opt_state = jax.jit(tx.init, backend="cpu")(params)
  
      rng, train_rng = jax.random.split(rng)
  
      # Create chrono class to track and store training statistics and metadata:
      chrono = train_utils.Chrono()
  
      train_state = train_utils.TrainState(
          global_step=0,
          opt_state=opt_state,
          tx=tx,
          params=params,
          model_state=model_state,
          rng=train_rng,
          metadata={"chrono": chrono.save()},
      )
      start_step = train_state.global_step
      if config.checkpoint:
          train_state, start_step = train_utils.restore_checkpoint(
              workdir, train_state
          )
      chrono.load(train_state.metadata["chrono"])
      train_state = train_state.replace(metadata={})
      if (
          start_step == 0  # Which means "no" checkpoint is restored!
          and config.get("init_from") is not None
      ):
          restored_model_cfg = config.init_from.get("model_config")
          init_checkpoint_path = config.init_from.get("checkpoint_path")
          if init_checkpoint_path is not None:
              restored_train_state = (
                  pretrain_utils.restore_pretrained_checkpoint(
                      init_checkpoint_path,
                      train_state,
                      assert_exist=True,
                  )
              )
              # Load params from the init_model.
              train_state = model.init_from_train_state(  # pytype: disable=attribute-error
                  train_state, restored_train_state, restored_model_cfg
              )
              del restored_train_state
      elif start_step == 0:
          logging.info(
              "Training completely from scratch."
              "Not restoring from any checkpoint."
          )
      return (
          train_state,
          chrono,
          lr_fn,
          start_step,
          num_trainable_params,
          gflops,
      )
  
imports: import functools
  from typing import Any, Callable, Dict, Iterator, Tuple, Optional, Type
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from clu import platform
  from flax import jax_utils
  import flax.linen
  import jax
  from jax.example_libraries.optimizers import clip_grads
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.svvit import metrics
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import optimizers
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
label_desc: Use image node_id 4 for calling a global function with example usage: init_state(rng, config, model, dataset, workdir) and returns: train_state,  chrono,  lr_fn,  start_step,  num_trainable_params,  gflops