code: def get_updates_and_outputs(ls):
      """Recognize and order the updates, outputs, and stopping condition for a `Scan`.
  
      WRITEME: what is the type of ls? how is it formatted?  if it's not in the
      predefined order already, how does this function know how to put it in that
      order?
  
      """
  
      def is_outputs(elem):
          if isinstance(elem, (list, tuple)) and all(
              isinstance(x, Variable) for x in elem
          ):
              return True
          if isinstance(elem, Variable):
              return True
          return False
  
      def is_updates(elem):
          if isinstance(elem, dict):
              # Make sure the updates will be applied in a deterministic order
              return True
          # Dictionaries can be given as lists of tuples
          if isinstance(elem, (list, tuple)) and all(
              isinstance(x, (list, tuple)) and len(x) == 2 for x in elem
          ):
              return True
          return False
  
      def is_condition(elem):
          return isinstance(elem, until)
  
      def _list(x):
          if isinstance(x, (list, tuple)):
              return list(x)
          else:
              return [x]
  
      def _filter(x):
          """
          Ensure `x` is made only of allowed data types.
  
          Return True iff `x` is made only of lists, tuples, dictionaries, PyTensor
          variables or `pytensor.scan.utils.until` objects.
  
          """
          # Is `x` a container we can iterate on?
          iter_on = None
          if isinstance(x, list) or isinstance(x, tuple):
              iter_on = x
          elif isinstance(x, dict):
              iter_on = x.items()
          if iter_on is not None:
              return all(_filter(y) for y in iter_on)
          else:
              return isinstance(x, Variable) or isinstance(x, until)
  
      if not _filter(ls):
          raise ValueError(
              "The return value of your scan lambda expression may only be "
              "made of lists, tuples, or dictionaries containing PyTensor "
              "variables (or `pytensor.scan.utils.until` objects for "
              "conditions). In particular if you need to use constant "
              "values, you can use `tensor.constant` to turn them into "
              "PyTensor variables."
          )
  
      if is_outputs(ls):
          return None, _list(ls), dict()
      if is_updates(ls):
          return None, [], dict(ls)
      error_msg = f"Scan cannot parse the return value of your lambda expression, which is: {ls}"
      if not isinstance(ls, (list, tuple)):
          raise ValueError(error_msg)
      ls = list(ls)
      deprecation_msg = (
          "The return value of the lambda function"
          " has been restricted. you have to always return first the"
          " outputs (if any), afterwards the updates (if any) and"
          " at the end the conclusion"
      )
      if len(ls) == 2:
          if is_outputs(ls[0]):
              if is_updates(ls[1]):
                  return (None, _list(ls[0]), dict(ls[1]))
              elif is_condition(ls[1]):
                  return (ls[1].condition, _list(ls[0]), dict())
              else:
                  raise ValueError(error_msg)
          elif is_updates(ls[0]):
              if is_outputs(ls[1]):
                  raise ValueError(deprecation_msg)
              elif is_condition(ls[1]):
                  return (ls[1].condition, [], dict(ls[0]))
              else:
                  raise ValueError(error_msg)
          else:
              raise ValueError(error_msg)
      elif len(ls) == 3:
          if is_outputs(ls[0]):
              if is_updates(ls[1]):
                  if is_condition(ls[2]):
                      return (
                          ls[2].condition,
                          _list(ls[0]),
                          dict(ls[1]),
                      )
                  else:
                      raise ValueError(error_msg)
              else:
                  raise ValueError(error_msg)
          else:
              raise ValueError(error_msg)
      else:
          raise ValueError(error_msg)
  
imports: import warnings
  import numpy
  import pytensor.tensor
  from pytensor.compile.function.pfunc import construct_pfunc_ins_and_outs
  from pytensor.compile.sharedvalue import SharedVariable, collect_new_shareds
  from pytensor.configdefaults import config
  from pytensor.graph.basic import Constant, Variable, graph_inputs
  from pytensor.graph.op import get_test_value
  from pytensor.graph.replace import clone_replace
  from pytensor.graph.utils import MissingInputError, TestValueError
  from pytensor.scan.op import Scan, ScanInfo
  from pytensor.scan.utils import expand_empty, safe_new, until
  from pytensor.tensor.basic import get_underlying_scalar_constant_value
  from pytensor.tensor.exceptions import NotScalarConstantError
  from pytensor.tensor.math import minimum
  from pytensor.tensor.shape import shape_padleft, unbroadcast
  from pytensor.tensor.type import TensorType, integer_dtypes
  from pytensor.updates import OrderedUpdates
label_desc: Use image node_id 1 for calling a global function with example usage: get_updates_and_outputs(ls) and returns: False, False, isinstance, None,  _list,  dict, None,  list,  dict, True, True, True, True, list, list, all, unknown, None,  _list,  dict, ls,  _list,  dict, ls,  list,  dict, ls,  _list,  dict