code: def load_encoder_params(
      train_state: train_utils.TrainState,
      config: ml_collections.ConfigDict,
  ):
      """Load encoder parameters."""
  
      if config.init_from.encoder.get("load_pretrained_weights", True):
          model_name = (
              config.model.encoder.cat_encoder.pretrained_config
          )
          t5_params = t5_model.load_pretrained_weights(model_name)
          model_prefix_path = ["video_encoder", "encoder_module"]
          if config.init_from.encoder.load_pretrained_weights:
              model_prefix_path = ["encoder"] + model_prefix_path
          train_state = load_utils.init_from_pretrain_weights(
              train_state,
              restored_params=t5_params,
              ckpt_prefix_path=["params", "t5_module", "encoder"],
              model_prefix_path=model_prefix_path,
          )
  
      return train_state
  
imports: import copy
  import dataclasses
  import functools
  import json
  import os
  import random
  from typing import Any, Callable, Dict, List, Optional, Tuple
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from dmvr import tokenizers
  from flax import jax_utils
  from flax.core import unfreeze
  import flax.linen
  import jax
  import jax.example_libraries.optimizers
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.t5 import model
  from scenic.projects.t5 import tokenizer
  from scenic.projects.vid2seq import load_utils
  from scenic.projects.vid2seq import models
  from scenic.projects.vid2seq import train_utils
  from scenic.projects.vid2seq import dvc_eval
  from scenic.train_lib_deprecated import lr_schedules
  from scenic.train_lib_deprecated import optimizers
  from scenic.train_lib_deprecated import pretrain_utils
  from scenic.train_lib_deprecated import train_utils
label_desc: Use image node_id 8 for calling a global function with example usage: load_encoder_params(train_state, config) and returns: train_state