code: def convert_clip_backbone(flax_params, torch_config):
      torch_model = CLIP(**torch_config)
      torch_model.eval()
      torch_clip_params = torch_model.state_dict()
  
      flax_clip_params = flatten_nested_dict(
          flax_params["backbone"]["clip"]
      )
      new_torch_params = {}
  
      for flax_key, v in flax_clip_params.items():
          torch_key = flax_key.replace("/", ".")
          torch_key = torch_key.replace(
              "text.token_embedding.embedding", "token_embedding.kernel"
          )
  
          if (
              torch_key.startswith("text.transformer")
              or torch_key.startswith("text.text_projection")
              or torch_key.startswith("text.ln_final")
              or torch_key.startswith("text.positional_embedding")
          ):
              torch_key = torch_key[5:]
  
          torch_key = torch_key.replace(
              "text_projection.kernel", "text_projection"
          )
          torch_key = torch_key.replace(
              "visual.proj.kernel", "visual.proj"
          )
          torch_key = torch_key.replace(".scale", ".weight")
          torch_key = torch_key.replace(".kernel", ".weight")
  
          if "conv" in torch_key or "downsample.0.weight" in torch_key:
              v = v.transpose(3, 2, 0, 1)
  
          elif (
              "weight" in torch_key
              and v.ndim == 2
              and "embedding" not in torch_key
          ):
              # Fully connected layers are transposed, embeddings are not
              v = v.T
  
          new_torch_params[torch_key] = v
  
      attn_params = _convert_attn_layers(new_torch_params)
      new_torch_params.update(attn_params)
      attn_params = {}
  
      # Copy flax CLIP backbone params to PyTorch params
      for name, param in new_torch_params.items():
          if name in torch_clip_params.keys():
              new_param = torch.from_numpy(new_torch_params[name])
              torch_clip_params[name].copy_(new_param)
          else:
              attn_params[name] = param
  
      return torch_clip_params, torch_model, attn_params
  
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 15 for calling a global function with example usage: convert_clip_backbone(flax_params, torch_config) and returns: torch_clip_params,  torch_model,  attn_params