code: def copy_class_merge_token(hf_model, flax_params):
      flax_class_token_params = flatten_nested_dict(
          flax_params["backbone"]["merged_class_token"]
      )
  
      weight = torch.from_numpy(flax_class_token_params["scale"])
      bias = torch.from_numpy(flax_class_token_params["bias"])
      hf_model.layer_norm.weight = nn.Parameter(weight)
      hf_model.layer_norm.bias = nn.Parameter(bias)
  
imports: import argparse
  import collections
  import jax
  import jax.numpy
  import torch
  import torch.nn
  from clip.model import CLIP
  from flax.training import checkpoints
  from huggingface_hub import Repository
  from transformers import CLIPTokenizer, OwlViTConfig, OwlViTForObjectDetection, OwlViTImageProcessor, OwlViTModel, OwlViTProcessor
label_desc: Use image node_id 11 for calling a global function with example usage: copy_class_merge_token(hf_model, flax_params) without return types