code: def while_loop(
      cond,
      body,
      loop_vars,
      maximum_iterations=None,
  ):
      loop_vars = tuple(loop_vars)
      if maximum_iterations is not None:
          current_iter = 0
          loop_vars = loop_vars + (current_iter,)
  
          # Unpack list/tuple args. The last argument is `current_iter`.
          def _cond(args):
              return cond(*args[:-1]) & (args[-1] < maximum_iterations)
  
          def _body(args):
              return tuple(body(*args[:-1])) + (args[-1] + 1,)
  
      else:
  
          def _cond(args):
              return cond(*args)
  
          def _body(args):
              return tuple(body(*args))
  
      outputs = jax.lax.while_loop(_cond, _body, loop_vars)
      if maximum_iterations is not None:
          outputs = outputs[:-1]
      return outputs
  
imports: import types
  import jax
  import jax.numpy
  import numpy
  import tree
  from jax.tree_util import Partial
  from keras.backend.common import KerasVariable
  from keras.backend.common import global_state
  from keras.backend.common import standardize_dtype
  from keras.backend.common.keras_tensor import KerasTensor
  from keras.backend.common.stateless_scope import StatelessScope
  from keras.backend.jax import distribution_lib
  from keras.utils.nest import pack_sequence_as
label_desc: Use image node_id 13 for calling a global function with example usage: while_loop(cond, body, loop_vars, maximum_iterations) and returns: outputs, unknown, unknown, cond, tuple