code: def get_model_and_tx_and_train_state(
      rng: RngType,
      dataset: dataset_utils.Dataset,
      config: ml_collections.ConfigDict,
      model_cls: Type[DeformableDETRModel],
      workdir: str,
      input_spec: InputSpec,
  ):
      """Create model and train state."""
      # Build the loss_and_metrics_fn, metrics, and flax_model.
      model = model_cls(config, dataset.meta_data)
  
      # Initialize model.
      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=input_spec,
          config=config,
          rngs=init_rng,
      )
  
      tx = get_optimizer(config, 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)
      train_state = TrainState(
          global_step=0,
          params=params,
          opt_state=opt_state,
          model_state=model_state,
          rng=train_rng,
      )
  
      start_step = train_state.global_step
      if config.checkpoint:
          train_state, start_step = train_utils.restore_checkpoint(
              workdir, train_state
          )
  
      if (
          start_step == 0  # Which means "no" checkpoint is restored!
          and config.get("init_from") is not None
      ):
          init_checkpoint_path = config.init_from.get("checkpoint_path")
          logging.info("Init from checkpoint: %s", init_checkpoint_path)
          restored_train_state = flax_restore_checkpoint(
              init_checkpoint_path, target=None
          )
          _handle_legacy_format(restored_train_state)
  
          train_state = pretrain_utils.init_from_pretrain_state(
              train_state,
              restored_train_state,
              ckpt_prefix_path=config.init_from.get("ckpt_prefix_path"),
              model_prefix_path=config.init_from.get(
                  "model_prefix_path"
              ),
              name_mapping=config.init_from.get("name_mapping"),
              skip_regex=config.init_from.get("skip_regex"),
          )
          # Free unecessary memory.
          del restored_train_state
      elif start_step == 0 and config.get(
          "load_pretrained_backbone", False
      ):
          # Only load pretrained backbone if we are at the beginning of training.
          bb_checkpoint_path = config.pretrained_backbone_configs.get(
              "checkpoint_path"
          )
          bb_train_state = flax_restore_checkpoint(
              bb_checkpoint_path, target=None
          )
          _handle_legacy_format(bb_train_state)
          train_state = pretrain_utils.init_from_pretrain_state(
              train_state, bb_train_state
          )
  
      # Replicate the optimzier, state, and rng.
      train_state = jax_utils.replicate(train_state)
      del params  # Do not keep a copy of the initial params.
  
      return (
          model,
          tx,
          train_state,
          num_trainable_params,
          gflops,
          start_step,
      )
  
imports: from collections.abc import Mapping
  from concurrent import futures
  import functools
  import time
  from typing import Any, Callable, Dict, Sequence, Tuple, Type, Union
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  import flax
  from flax import jax_utils
  from flax.training.checkpoints import restore_checkpoint
  import jax
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.projects.baselines.deformable_detr import coco_eval
  from scenic.projects.baselines.deformable_detr import evaluate
  from scenic.projects.baselines.deformable_detr.model import DeformableDETRModel
  from scenic.projects.baselines.detr import train_utils
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
  from scenic.train_lib.train_utils import PyTree
  from scenic.train_lib.train_utils import TrainState
label_desc: Use image node_id 4 for calling a global function with example usage: get_model_and_tx_and_train_state(rng, dataset, config, model_cls, workdir, input_spec) and returns: model,  tx,  train_state,  num_trainable_params,  gflops,  start_step