class_docstr: Block of reversible layers. See rev_block.
class_docstr_tok: ["Block","of","reversible","layers",".","See","rev_block","."]
docstr: Run forward through the reversible layers.
docstr_tok: ["Run","forward","through","the","reversible","layers","."]
code: def _forward(self, x1, x2):
      """Run forward through the reversible layers."""
  
      side_inputs = [self.f_side_input, self.g_side_input]
      flat_side_inputs = nest.flatten(side_inputs)
  
      def _forward_wrap(x1_, x2_, *flat_side_inputs):
          f_side, g_side = nest.pack_sequence_as(
              side_inputs, flat_side_inputs
          )
          return _rev_block_forward(
              x1_,
              x2_,
              self.f,
              self.g,
              num_layers=self.num_layers,
              f_side_input=f_side,
              g_side_input=g_side,
              gate_outputs=self._use_efficient_backprop,
          )
  
      @custom_gradient.custom_gradient
      def _forward_with_custom_grad(*args):
          out = _forward_wrap(
              *args
          )  # pylint: disable=no-value-for-parameter
          grad_fn = self._make_efficient_grad_fn(args, out)
          return out, grad_fn
  
      if self._use_efficient_backprop:
          return _forward_with_custom_grad(x1, x2, *flat_side_inputs)
      else:
          return _forward_wrap(x1, x2, *flat_side_inputs)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import re
  import numpy
  import six
  from six.moves import xrange
  from ..None import contrib_framework
  from tensorflow.python.eager import backprop
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.layers import base
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import control_flow_util
  from tensorflow.python.ops import custom_gradient
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util import nest
  from tensorflow.python.util import tf_inspect
label_desc: Use image node_id 7 for calling the RevBlock obj's underlying member method code with example usage: obj._forward(x1, x2) and returns: _rev_block_forward, out,  grad_fn, _forward_with_custom_grad, _forward_wrap