code: def load_clip_params(random_params, model_name, temporal_agg):
      """Load CLIP parameters."""
      logging.info("Loading CLIP weights...")
      clip_vars = clip.load_model_vars(model_name)
      clip_params = clip_vars["params"]
      params = {
          "text_encoder": dict(TextTower=clip_params["text"]),
          "video_encoder": dict(
              Image2VideoEncoder_0=dict(
                  ImageTower=clip_params["visual"]
              )
          ),
      }
      # seqTrans Clip4Clip initializes the transformer temporal aggregation
      # module with weights from CLIP Text tower.
      if temporal_agg == "transformer":
          clip_text = clip_params["text"]["transformer"]
          num_layer_transformer = len(
              random_params["video_encoder"]["Image2VideoEncoder_0"][
                  "seqTrans_transformer"
              ]
          )  # pylint: disable=line-too-long
          params["video_encoder"]["Image2VideoEncoder_0"][
              "seqTrans_transformer"
          ] = {
              "resblocks." + str(n): clip_text["resblocks." + str(n)]
              for n in range(num_layer_transformer)
              if n < len(clip_text)  # pylint: disable=line-too-long
          }
          number_of_frame = random_params["video_encoder"][
              "Image2VideoEncoder_0"
          ]["seqTrans_positional_embedding"].shape[
              0
          ]  # pylint: disable=line-too-long
          params["video_encoder"]["Image2VideoEncoder_0"][
              "seqTrans_positional_embedding"
          ] = clip_params["text"]["positional_embedding"][
              :number_of_frame
          ]  # pylint: disable=line-too-long
      params = jax.tree_util.tree_map(
          lambda x: jnp.array(x, dtype=jnp.float32), params
      )
      return replace_dict(random_params, params, name_mapping={})
  
imports: import os
  import re
  from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
  from absl import logging
  import flax
  from flax import struct
  from flax.core import frozen_dict
  import flax.linen
  from flax.training import checkpoints
  import jax
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.baselines.clip import model
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import optimizers
  from scenic.train_lib import train_utils
  from tensorflow.io import gfile
label_desc: Use image node_id 4 for calling a global function with example usage: load_clip_params(random_params, model_name, temporal_agg) and returns: replace_dict