code: def scan_checkpoints(
      fn,
      sequences=None,
      outputs_info=None,
      non_sequences=None,
      name="checkpointscan_fn",
      n_steps=None,
      save_every_N=10,
      padding=True,
  ):
      """Scan function that uses less memory, but is more restrictive.
  
      In :func:`~pytensor.scan`, if you compute the gradient of the output
      with respect to the input, you will have to store the intermediate
      results at each time step, which can be prohibitively huge. This
      function allows to do ``save_every_N`` steps of forward computations
      without storing the intermediate results, and to recompute them during
      the gradient computation.
  
      Notes
      -----
      Current assumptions:
  
      * Every sequence has the same length.
      * If ``n_steps`` is specified, it has the same value as the length of
        any sequence.
      * The value of ``save_every_N`` divides the number of steps the scan
        will run without remainder.
      * Only singly-recurrent and non-recurrent outputs are used.
        No multiple recurrences.
      * Only the last timestep of any output will ever be used.
  
      Parameters
      ----------
      fn
          ``fn`` is a function that describes the operations involved in one
          step of ``scan``. See the documentation of :func:`~pytensor.scan`
          for more information.
  
      sequences
          ``sequences`` is the list of PyTensor variables or dictionaries
          describing the sequences ``scan`` has to iterate over. All
          sequences must be the same length in this version of ``scan``.
  
      outputs_info
          ``outputs_info`` is the list of PyTensor variables or dictionaries
          describing the initial state of the outputs computed
          recurrently.
  
      non_sequences
          ``non_sequences`` is the list of arguments that are passed to
          ``fn`` at each steps. One can opt to exclude variable
          used in ``fn`` from this list as long as they are part of the
          computational graph, though for clarity we encourage not to do so.
  
      n_steps
          ``n_steps`` is the number of steps to iterate given as an int
          or PyTensor scalar (> 0). If any of the input sequences do not have
          enough elements, scan will raise an error. If n_steps is not provided,
          ``scan`` will figure out the amount of steps it should run given its
          input sequences.
  
      save_every_N
          ``save_every_N`` is the number of steps to go without storing
          the computations of ``scan`` (ie they will have to be recomputed
          during the gradient computation).
  
      padding
          If the length of the sequences is not a multiple of ``save_every_N``,
          the sequences will be zero padded to make this version of ``scan``
          work properly, but will also result in a memory copy. It can be
          avoided by setting ``padding`` to False, but you need to make
          sure the length of the sequences is a multiple of ``save_every_N``.
  
      Returns
      -------
      tuple
          Tuple of the form ``(outputs, updates)`` as in :func:`~pytensor.scan`, but
          with a small change: It only contain the output at each
          ``save_every_N`` step. The time steps that are not returned by
          this function will be recomputed during the gradient computation
          (if any).
  
      See Also
      --------
      :func:`~pytensor.scan`: Looping in PyTensor.
  
      """
      # Standardize the format of input arguments
      if sequences is None:
          sequences = []
      elif not isinstance(sequences, list):
          sequences = [sequences]
  
      if not isinstance(outputs_info, list):
          outputs_info = [outputs_info]
  
      if non_sequences is None:
          non_sequences = []
      elif not isinstance(non_sequences, list):
          non_sequences = [non_sequences]
  
      # Check that outputs_info has no taps:
      for element in outputs_info:
          if isinstance(element, dict) and "taps" in element:
              raise RuntimeError(
                  "scan_checkpoints doesn't work with taps."
              )
  
      # Determine how many steps the original scan would run
      if n_steps is None:
          n_steps = sequences[0].shape[0]
  
      # Compute the number of steps of the outer scan
      o_n_steps = ptb.cast(ceil(n_steps / save_every_N), "int64")
  
      # Compute the number of steps of the inner scan
      i_n_steps = save_every_N * ptb.ones((o_n_steps,), "int64")
      mod = n_steps % save_every_N
      last_n_steps = ptb.switch(eq(mod, 0), save_every_N, mod)
      i_n_steps = set_subtensor(i_n_steps[-1], last_n_steps)
  
      # Pad the sequences if needed
      if padding:
          # Since padding could be an empty tensor, Join returns a view of s.
          join = Join(view=0)
          for i, s in enumerate(sequences):
              n = s.shape[0] % save_every_N
              z = ptb.zeros((n, s.shape[1:]), dtype=s.dtype)
              sequences[i] = join(0, [s, z])
  
      # Establish the input variables of the outer scan
      o_sequences = [
          s.reshape(
              [s.shape[0] / save_every_N, save_every_N]
              + [s.shape[i] for i in range(1, s.ndim)],
              s.ndim + 1,
          )
          for s in sequences
      ]
      o_sequences.append(i_n_steps)
      new_nitsots = [i for i in outputs_info if i is None]
      o_nonsequences = non_sequences
  
      def outer_step(*args):
          # Separate the received arguments into their respective (seq, outputs
          # from previous iterations, nonseqs) categories
          i_sequences = list(args[: len(o_sequences)])
          i_prev_outputs = list(
              args[len(o_sequences) : -len(o_nonsequences)]
          )
          i_non_sequences = list(args[-len(o_nonsequences) :])
          i_outputs_infos = i_prev_outputs + [
              None,
          ] * len(new_nitsots)
  
          # Call the user-provided function with the proper arguments
          results, updates = scan(
              fn=fn,
              sequences=i_sequences[:-1],
              outputs_info=i_outputs_infos,
              non_sequences=i_non_sequences,
              name=name + "_inner",
              n_steps=i_sequences[-1],
          )
          if not isinstance(results, list):
              results = [results]
  
          # Keep only the last timestep of every output but keep all the updates
          if not isinstance(results, list):
              return results[-1], updates
          else:
              return [r[-1] for r in results], updates
  
      results, updates = scan(
          fn=outer_step,
          sequences=o_sequences,
          outputs_info=outputs_info,
          non_sequences=o_nonsequences,
          name=name + "_outer",
          n_steps=o_n_steps,
          allow_gc=True,
      )
  
      return results, updates
  
imports: import pytensor.tensor.basic
  from pytensor.scan.basic import scan
  from pytensor.tensor.basic import Join
  from pytensor.tensor.math import ceil, eq
  from pytensor.tensor.subtensor import set_subtensor
label_desc: Use image node_id 1 for calling a global function with example usage: scan_checkpoints(fn, sequences, outputs_info, non_sequences, name, n_steps, save_every_N, padding) and returns: results,  updates, results,  updates, unknown,  updates