[content] train_dynamics.py (python)
        "model_state", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler
    )
    handler_registry.add(
        "model_state", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler
    )
    handler_registry.add("dataloader_state", grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler)  # type: ignore
    handler_registry.add("dataloader_state", grain.checkpoint.CheckpointRestore, grain.checkpoint.CheckpointHandler)  # type: ignore

    checkpoint_options = ocp.CheckpointManagerOptions(
        save_interval_steps=args.log_checkpoint_interval,
        max_to_keep=3,
        keep_period=args.log_checkpoint_keep_period,
        step_format_fixed_length=6,
        cleanup_tmp_directories=True,
    )

    checkpoint_manager = ocp.CheckpointManager(
        args.ckpt_dir,
        options=checkpoint_options,
        handler_registry=handler_registry,
    )

    # --- Create DataLoaderIterator from dataloader ---
    image_shape = (args.image_height, args.image_width, args.image_channels)
    array_record_files = [
        os.path.join(args.data_dir, x)
        for x in os.listdir(args.data_dir)
        if x.endswith(".array_record")
    ]
    grain_dataloader = get_dataloader(
        array_record_files,
        args.seq_len,
        # NOTE: We deliberately pass the global batch size
        # The dataloader shards the dataset across all processes
        args.batch_size,
        *image_shape,
        num_workers=8,
        prefetch_buffer_size=1,
        seed=args.seed,
    )
    initial_state = grain_dataloader._create_initial_state()
    grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)

    # --- Restore checkpoint ---
    if args.restore_ckpt:
        # FIXME (f.srambical): pass sharding information for restoration onto different topology
        abstract_optimizer = nnx.eval_shape(lambda: optimizer)
        abstract_optimizer_state = nnx.state(abstract_optimizer)
        restored = checkpoint_manager.restore(
            checkpoint_manager.latest_step(),
            args=ocp.args.Composite(
                model_state=ocp.args.PyTreeRestore(abstract_optimizer_state, partial_restore=True),
                dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator),
            ),
        )
        restored_optimizer_state = restored["model_state"]
        nnx.update(optimizer, restored_optimizer_state)
        grain_iterator = restored["dataloader_state"]
        step = checkpoint_manager.latest_step() or 0
        print(f"Restored dataloader and model state from step {step}")
    else:
        # Restore from pre-trained tokenizer (and LAM)
        optimizer = restore_genie_components(
            optimizer, replicated_sharding, rng, args
        )

    # --- TRAIN LOOP ---
    dataloader = (jax.make_array_from_process_local_data(videos_sharding, elem) for elem in grain_iterator)  # type: ignore
    print(f"Starting training from step {step}...")
    while step < args.num_steps:
        for videos in dataloader:
            # --- Train step ---
            rng, _rng_mask = jax.random.split(rng, 2)
            inputs = dict(videos=videos, mask_rng=_rng_mask)
            loss, recon, metrics = train_step(genie, optimizer, inputs)
            metrics["lr"] = lr_schedule(step)
            print(f"Step {step}, loss: {loss}")
            step += 1

            # --- Logging ---
            if args.log:
                if step % args.log_interval == 0 and jax.process_index() == 0:
                    wandb.log(
                        {
                            "loss": loss,
                            "step": step,
                            **metrics,
                        }
                    )
                if step % args.log_image_interval == 0:
                    gt_seq = inputs["videos"][0].astype(jnp.float32) / 255.0
                    recon_seq = recon[0].clip(0, 1)
                    comparison_seq = jnp.concatenate((gt_seq, recon_seq), axis=1)
                    comparison_seq = einops.rearrange(
                        comparison_seq * 255, "t h w c -> h (t w) c"
                    )
                    if jax.process_index() == 0:
                        log_images = dict(
                            image=wandb.Image(np.asarray(gt_seq[args.seq_len - 1])),
                            recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),
                            true_vs_recon=wandb.Image(
                                np.asarray(comparison_seq.astype(np.uint8))
                            ),
                        )
                        wandb.log(log_images)
            # --- Checkpointing ---
            if args.save_ckpt and step % args.log_checkpoint_interval == 0:
                optimizer_state = nnx.state(optimizer)
                checkpoint_manager.save(
                    step,
                    args=ocp.args.Composite(
                        model_state=ocp.args.PyTreeSave(optimizer_state),