code: def _convert_attn_layers(
      params: Mapping[str, np.ndarray], dim_head: int = 64
  ) -> PyTree:
      """Convert attention parameters."""
      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_proj.bias"].shape[-1]
              heads = dim // dim_head
              new_params[base + "out.weight"] = params[
                  base + "out_proj.weight"
              ].T.reshape(heads, dim_head, dim)
              new_params[base + "out.bias"] = params[
                  base + "out_proj.bias"
              ]
              qkv_bias = params[base + "in_proj_bias"].reshape(
                  3, heads, dim_head
              )
              qkv_kernel = np.transpose(
                  params[base + "in_proj_weight"].reshape(
                      3, heads, dim_head, dim
                  ),
                  (0, 3, 1, 2),
              )
              for i, kk in enumerate(("query", "key", "value")):
                  new_params[base + f"{kk}.bias"] = qkv_bias[i]
                  new_params[base + f"{kk}.weight"] = qkv_kernel[i]
          else:
              new_params[k] = v
      return new_params
  
imports: import os
  from typing import Any, Mapping, Optional
  from absl import logging
  import flax
  import jax
  import jax.numpy
  import numpy
  from scenic.projects.owl_vit.clip import layers
  from scenic.projects.baselines.clip import download
  from tensorflow.io import gfile
label_desc: Use image node_id 10 for calling a global function with example usage: _convert_attn_layers(params, dim_head) and returns: new_params