code: def _convert_attn_layers(params):
      new_params = {}
      processed_attn_layers = []
  
      for k, v in params.items():
          if "attn." in k:
              base = k[: k.rindex("attn.") + 5]
              if base in processed_attn_layers:
                  continue
  
              processed_attn_layers.append(base)
              dim = params[base + "out.weight"].shape[-1]
              new_params[base + "out_proj.weight"] = (
                  params[base + "out.weight"].reshape(dim, dim).T
              )
              new_params[base + "out_proj.bias"] = params[
                  base + "out.bias"
              ]
          else:
              new_params[k] = v
      return new_params
  
imports: import argparse
  import collections
  import torch
  import torch.nn
  import jax
  import jax.numpy
  from clip.model import CLIP
  from flax.training import checkpoints
  from huggingface_hub import Repository
  from transformers import CLIPTokenizer, OwlViTConfig, OwlViTFeatureExtractor, OwlViTForObjectDetection, OwlViTModel, OwlViTProcessor
label_desc: Use image node_id 14 for calling a global function with example usage: _convert_attn_layers(params) and returns: new_params