code: def convert_pre_linen(params: PyTree) -> PyTree:
      """Converts a pre-Linen parameter pytree.
  
      In pre-Linen API submodules were numbered incrementally, independent of the
      submodule class. With Linen this behavior has changed to keep separate
      submodule counts per module class.
  
      Consider the following module::
  
        class Model(nn.Module):
          @nn.compact
          def __call__(self, x):
            x = nn.Conv(1, 1)(x)
            x = nn.Dense(1)(x)
            return x
  
      In pre-Linen the resulting params would have had the structure:
  
        ``{'Conv_0': { ... }, 'Dense_1': { ... } }``
  
      With Linen the resulting params would instead have had the structure:
  
        ``{'Conv_0': { ... }, 'Dense_0': { ... } }``
  
      To convert from pre-Linen format to Linen simply call::
  
        params = convert_pre_linen(pre_linen_params)
  
      Note that you can also use this utility to convert pre-Linen collections
      because they're following the same module naming. Note though that collections
      were "flat" in pre-Linen and first need to be unflattened before they can be
      used with this function::
  
        batch_stats = convert_pre_linen(flax.traverse_util.unflatten_dict({
            tuple(k.split('/')[1:]): v
            for k, v in pre_linen_model_state.as_dict().items()
        }))
  
      Then Linen variables can be defined from these converted collections::
  
        variables = {'params': params, 'batch_stats': batch_stats}
  
      Args:
        params: Parameter pytree in pre-Linen format. If the pytree is already in
          Linen format, then the returned pytree is unchanged (i.e. this function
          can safely be called on any loaded checkpoint for use with Linen).
  
      Returns:
        Parameter pytree with Linen submodule naming.
      """
      if not isinstance(params, (dict, core.FrozenDict)):
          return params
      params_renamed = {}
      counts: Dict[Any, Any] = {}
      names = natural_sort(params.keys())
      for name in names:
          value = params[name]
          match = MODULE_NUM_RE.match(name)
          if match:
              module = match.group(1)
              num = counts.get(module, 0)
              name = f"{module}_{num}"
              counts[module] = num + 1
          params_renamed[name] = convert_pre_linen(value)
  
      if isinstance(params, core.FrozenDict):
          params_renamed = core.freeze(params_renamed)  # type: ignore
      return params_renamed
  
imports: import functools
  import os
  import pathlib
  import re
  import time
  import warnings
  from concurrent.futures import thread
  from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type, Union
  import jax
  import orbax.checkpoint
  from absl import logging
  from jax import monitoring, process_index
  from jax import tree_util
  from jax.experimental.array_serialization.serialization import GlobalAsyncCheckpointManager, get_tensorstore_spec
  from jax.experimental.multihost_utils import sync_global_devices
  from flax import config, core, errors, io, serialization, traverse_util
  from flax.training import orbax_utils
label_desc: Use image node_id 24 for calling a global function with example usage: convert_pre_linen(params) and returns: params_renamed, params