code: def load_key_params(
      params: constants.PyTree, config: ml_collections.ConfigDict
  ):
      """Load T5 & ViT for Key Encoders."""
      t5_params = t5_model.load_pretrained_weights(config.model.t5_name)
      if "key_text_encoder" in params:
          params = replace_dict(
              params,
              t5_params,
              ckpt_prefix_path=["params", "t5_module", "encoder"],
              model_prefix_path=["key_text_encoder"],
              name_mapping={},
          )
      return params
  
imports: import dataclasses
  import operator
  import re
  from typing import Any, Dict, List, Mapping, Optional, Tuple, Union
  from absl import logging
  from big_vision import utils
  from big_vision.models import vit
  from clu import metric_writers
  import flax
  from flax import jax_utils
  from flax.core import frozen_dict
  import jax
  import jax.numpy
  import ml_collections
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib import datasets
  from scenic.projects.knowledge_visual_language.models import constants
  from scenic.projects.t5 import model
  from scenic.train_lib import train_utils
  from scenic.train_lib.train_utils import TrainState
label_desc: Use image node_id 8 for calling a global function with example usage: load_key_params(params, config) and returns: params