class_docstr: Hierarchical softmax function based on a binary tree.
  
  This function object should be allocated beforehand, and be copied on every
  forward computation, since the initializer parses the given tree. See the
  implementation of :class:`BinaryHierarchicalSoftmax` for details.
  
  Args:
      tree: A binary tree made with tuples like ``((1, 2), 3)``.
  
  .. seealso::
     See :class:`BinaryHierarchicalSoftmax` for details.
code: def device_resident_accept(self, visitor):
      super(
          BinaryHierarchicalSoftmaxFunction, self
      ).device_resident_accept(visitor)
      self.paths = visitor.visit_array(self.paths)
      self.codes = visitor.visit_array(self.codes)
      self.begins = visitor.visit_array(self.begins)
  
imports: import copy
  import numpy
  import six
  import chainer
  from chainer.backends import cuda
  from chainer import device_resident
  from chainer import function
  from chainer.initializers import uniform
  from chainer import link
  from chainer import utils
  from chainer.utils import type_check
  from chainer import variable
label_desc: Use image node_id 3 for calling the BinaryHierarchicalSoftmaxFunction obj's underlying member method code with example usage: obj.device_resident_accept(visitor) without return types