code: def get_vit_clip_config(model_name: str) -> ml_collections.ConfigDict:
      configs = clip.CONFIGS[model_name]
      return ml_collections.ConfigDict(
          {
              "patch_size": configs["vision_patch_size"],
              "features": configs["vision_features"],
              "num_layers": configs["vision_num_layers"],
              "num_heads": configs["vision_features"] // 64,
              "out_features": configs["embed_dim"],
          }
      )
  
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 1 for calling a global function with example usage: get_vit_clip_config(model_name) and returns: ml_collections