code: def maybe_recover_lstm_type(
      state: types.NestedArray,
  ) -> types.NestedArray:
      """Recovers the type hk.LSTMState if LSTMState is in the type name.
  
      When the recurrent state of recurrent neural networks (RNN) is deserialized,
      for example when it is sampled from replay, it is sometimes repacked in a type
      that is identical to the source type but not the correct type itself. When
      using this state as the initial state in an hk.dynamic_unroll, this will
      cause hk.dynamic_unroll to raise an error as it requires its input and output
      states to be identical.
  
      Args:
        state: a nested structure of arrays representing the state of an RNN.
  
      Returns:
        Either the state unchanged if it is anything but an LSTMState, otherwise
        returns the state arrays properly contained in an hk.LSTMState.
      """
      return (
          hk.LSTMState(*state)
          if type(state).__name__ == "LSTMState"
          else state
      )
  
imports: import functools
  import itertools
  import queue
  import threading
  from typing import Callable, Iterable, Iterator, NamedTuple, Optional, Sequence, Tuple, TypeVar
  from absl import logging
  from acme import core
  from acme import types
  from acme.jax import types
  import haiku
  import jax
  import jax.numpy
  import numpy
  import reverb
  import tree
label_desc: Use image node_id 14 for calling a global function with example usage: maybe_recover_lstm_type(state) and returns: unknown