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.baselines.clip import layers
  from scenic.projects.baselines.clip import download
  from tensorflow.io import gfile
label_desc: Use image node_id 11 for calling a global function with example usage: _convert_attn_layers(params, dim_head) and returns: new_params