code: def init_state(
      model: base_model.BaseModel,
      dataset: dataset_utils.Dataset,
      config: ml_collections.ConfigDict,
      workdir: str,
      rng: jnp.ndarray,
      writer: metric_writers.MetricWriter,
  ):
      """Initialize the train state."""
  
      input_spec = {
          key[:-5]: dataset.meta_data[key]
          for key in dataset.meta_data
          if key[-5:] == "_spec"
      }
      # Initialize model.
      rng, init_rng = jax.random.split(rng)
      (
          params,
          model_state,
          num_params,
          gflops,
      ) = train_utils.initialize_model_with_pytree(
          model_def=model.flax_model,
          input_spec=input_spec,
          config=config,
          rngs=init_rng,
      )
      logging.info("The model has %d params", num_params)
  
      if gflops:
          logging.info("uses %d gflops", gflops or -1)
      lr_fn = lr_schedules.get_learning_rate_fn(config)
      # Create the optimizer.
      # 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].
      tx = optimizers.get_optimizer(
          optimizer_config=optimizers.get_optax_optimizer_config(
              config
          ),
          learning_rate_fn=lr_fn,
      )
      tx = trainer_utils.froze_param_optax(
          params=params,
          tx=tx,
          frozen_patterns=config.get("frozen_patterns", None),
          not_frozen_patterns=config.get("not_frozen_patterns", None),
      )
      opt_state = jax.jit(tx.init, backend="cpu")(params)
      # del params  # Do not keep a copy of the initial params.
  
      _, train_rng = jax.random.split(rng)
      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()},
      )
      if config.checkpoint:
          logging.info("Continuing training from the checkpoint")
          logging.info("workdir= %s", workdir)
          train_state, params_axes = trainer_utils.pop_axes_names(
              train_state, axes_name="params_axes"
          )
          train_state, _ = train_utils.restore_checkpoint(
              workdir, train_state
          )
          train_state = trainer_utils.re_add_axis_names(
              train_state, params_axes, axes_name="params_axes"
          )
  
      start_step = int(train_state.global_step)
      chrono.load(train_state.metadata["chrono"])
  
      if start_step == 0:
          if config.get("init_from", False):
              if config.init_from.get("resume", False):
                  workdir = config.init_from.get("resume")
                  logging.info("Resuming training from the checkpoint")
                  logging.info("workdir= %s", workdir)
                  (
                      train_state,
                      params_axes,
                  ) = trainer_utils.pop_axes_names(
                      train_state, axes_name="params_axes"
                  )
                  train_state, _ = train_utils.restore_checkpoint(
                      workdir, train_state
                  )
                  train_state = trainer_utils.re_add_axis_names(
                      train_state, params_axes, axes_name="params_axes"
                  )
                  start_step = int(train_state.global_step)
                  chrono.load(train_state.metadata["chrono"])
  
              elif config.init_from.get("xm", False):
                  if config.init_from.load_key_encoder:
                      params = trainer_utils.load_key_params(
                          params, config
                      )
                      train_state = train_state.replace(  # pytype: disable=attribute-error
                          params=params
                      )
              else:
                  logging.info(
                      "Loading T5 & ViT Parameter from Pre-Trained Model"
                  )
                  params = trainer_utils.load_visual_params(
                      params, config
                  )
                  params = trainer_utils.load_text_params(
                      params, config
                  )
                  train_state = train_state.replace(  # pytype: disable=attribute-error
                      params=params
                  )
          step0_log = {"num_trainable_params": num_params}
          if gflops:
              step0_log["gflops"] = gflops
          writer.write_scalars(1, step0_log)
  
      return train_state, start_step, chrono, lr_fn
  
imports: import functools
  from typing import Any, Dict, Optional, Tuple
  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
  import jax.example_libraries.optimizers
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.xm import xm_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.knowledge_visual_language import trainer_utils
  from scenic.projects.knowledge_visual_language.models import constants
  from scenic.projects.knowledge_visual_language.models import losses
  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 1 for calling a global function with example usage: init_state(model, dataset, config, workdir, rng, writer) and returns: train_state,  start_step,  chrono,  lr_fn