code: def init_state(
      model: base_model.BaseModel,
      dataset: dataset_utils.Dataset,
      config: ml_collections.ConfigDict,
      workdir: str,
      rng: jnp.ndarray,
  ):
      """Initialize the model state."""
      input_shapes = dataset.meta_data["input_shape"]
      input_dtype = dataset.meta_data.get("input_dtype", jnp.float32)
      final_spec_list = [
          (input_shapes["rgb"], input_dtype),
          (
              dataset.meta_data["text_shape"],
              dataset.meta_data["text_dtype"],
          ),
      ]
      # Initialize model.
      rng, init_rng = jax.random.split(rng)
      (
          params,
          model_state,
          num_params,
          gflops,
      ) = train_utils.initialize_model(
          model_def=model.flax_model,
          input_spec=final_spec_list,
          config=config,
          rngs=init_rng,
      )
      logging.info("The model has %d params", num_params)
      if gflops is not None:
          logging.info("The model uses %d gflops", gflops)
  
      chrono = train_utils.Chrono()
      # Create optimizer.
      tx, opt_state = build_optimizer(config, params)
      # Create train state.
      train_state = OptaxTrainState(
          global_step=0,
          opt_state=opt_state,
          tx=tx,
          model_state=model_state,
          weights=params,
          rng=rng,
          metadata={"chrono": chrono.save()},
      )
  
      start_step = train_state.global_step
      if config.checkpoint:
          train_state, param_axes = pop_axes_names(
              train_state, axes_name="params_axes"
          )
          train_state, start_step = restore_checkpoint(
              workdir, train_state
          )
          train_state = re_add_axis_names(
              train_state,
              param_axes=param_axes,
              axes_name="params_axes",
          )
      new_params = None
      if start_step == 0 and not config.get("train_from_scratch"):
          if config.init_from.get("checkpoint_path", None):
              checkpoint_path = config.init_from.checkpoint_path
              logging.info("Loading weights from %s", checkpoint_path)
              new_params = checkpoints.restore_checkpoint(
                  checkpoint_path, None
              )
              if "weights" in new_params:
                  new_params = new_params["weights"]
          else:
              new_params = load_clip_params(
                  train_state.weights,
                  config.model.clip_version,
                  config.model.temporal_agg,
              )
      if new_params is not None:
          tx, opt_state = build_optimizer(config, new_params)
          train_state = train_state.replace(
              tx=tx, opt_state=opt_state, weights=new_params
          )
          logging.info("Weights succesfully loaded.")
      elif start_step == 0:
          logging.info(
              "Training completely from scratch. "
              "Not restoring from any checkpoint."
          )
      return train_state, start_step, chrono
  
imports: import os
  import re
  from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
  from absl import logging
  import flax
  from flax import struct
  from flax.core import frozen_dict
  import flax.linen
  from flax.training import checkpoints
  import jax
  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.baselines.clip import model
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import optimizers
  from scenic.train_lib import train_utils
  from tensorflow.io import gfile
label_desc: Use image node_id 3 for calling a global function with example usage: init_state(model, dataset, config, workdir, rng) and returns: train_state,  start_step,  chrono