code: def extract_reversible_blocks(layers, loss_chunk_size=0):
      """Extracts blocks and loss layer for use with ReversibleSerialTrainer.
  
      Args:
        layers: a list of layers of a single layer to extract blocks from;
          should end with a loss, e.g., [model, loss] or tl.Serial(model, loss).
        loss_chunk_size: int, if > 0 creates a chunked loss layer to save memory
          in models with larger vocabulary; requires the last sublayers of loss
          are [Dense, LogSoftmax, _CrossEntropy, _WeightedMean] in that order.
  
      Returns:
        a pair (blocks, loss_layer) to use with ReversibleSerialTrainer.
      """
  
      def _flatten(l):
          """Flatten all Serial layers and sub(sub-...) layers into a list."""
          if isinstance(l, (list, tuple)):
              return [
                  x for layer in l for x in _flatten(layer)
              ]  # pylint: disable=g-complex-comprehension
          elif isinstance(l, tl.Serial):
              return _flatten(l.sublayers)
          else:
              return [l]
  
      # Extract standard and reversible layer blocks.
      blocks, std_layers, rev_layers = [], [], []
      for layer in _flatten(layers):
          if isinstance(layer, tl.ReversibleLayer):
              rev_layers.append(layer)
          elif not rev_layers:
              std_layers.append(layer)
          else:
              blocks.append((std_layers, rev_layers))
              std_layers, rev_layers = [], []
              std_layers.append(layer)
      if rev_layers:
          raise ValueError(
              "The final layer must be a standard loss, not reversible."
          )
      if loss_chunk_size > 0:
          # For now we only do chunking of [Dense, LogSoftmax, CrossEntopy, Mean]
          # Let's check that these are the last 4 layers.
          border_layers = ["StripFromConcatenateWithPadding", "Select"]
  
          loss_start = None
          for index, layer in enumerate(std_layers):
              if layer.name in border_layers:
                  loss_start = index + 1
          if loss_start is None:
              raise ValueError(
                  "Loss layer should be preceeded by one of {}; got {}".format(
                      border_layers, [l.name for l in std_layers]
                  )
              )
          if len(std_layers) - loss_start < 4:
              raise ValueError("Too short loss layer for chunking")
          last_3_names = " ".join([l.name for l in std_layers[-3:]])
          if last_3_names != "LogSoftmax _CrossEntropy _WeightedMean":
              raise ValueError(
                  'Loss chunking only works with last layers being "'
                  'LogSoftmax, _CrossEntropy, _WeightedMean" but got: '
                  + last_3_names
              )
  
          # Create chunked dense+logsoftmax+cross-entropy-loss.
          chunked_xent = tl.Chunk(
              tl.Serial(std_layers[loss_start:-1]), loss_chunk_size
          )
  
          # The chunked loss should operate on a merged batch dimension, e.g.,
          # including both length and batch size. Need to merge and un-merge later.
          def _reshape_to_batch_and_copy_targets(preds, targets):
              batched_preds = jnp.reshape(preds, [-1, preds.shape[-1]])
              batched_targets = jnp.reshape(targets, [-1])
              return batched_preds, batched_targets, targets
  
          def _reshape_xent_back(xent, targets):
              return jnp.reshape(xent, targets.shape)
  
          batched_xent = tl.Serial(
              tl.Fn(
                  "pre_xent_rebatch",
                  _reshape_to_batch_and_copy_targets,
                  n_out=3,
              ),
              chunked_xent,
              tl.Fn("after_xent_rebatch", _reshape_xent_back),
          )
          loss_layer = tl.Serial(
              std_layers[:loss_start] + [batched_xent], std_layers[-1]
          )
      else:
          loss_layer = tl.Serial(std_layers)
      return blocks, loss_layer
  
imports: from concurrent import futures
  import functools
  import os
  import time
  from absl import logging
  import jax
  import numpy
  import psutil
  from trax import fastmath
  from trax import layers
  from trax.fastmath import numpy
  from trax.layers import base
  from trax.layers import combinators
label_desc: Use image node_id 7 for calling a global function with example usage: extract_reversible_blocks(layers, loss_chunk_size) and returns: blocks,  loss_layer, unknown, batched_preds,  batched_targets,  targets, jnp, _flatten, list