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 forward_cpu(self, inputs):
      x, t, W = inputs
  
      loss = x.dtype.type(0.0)
      for ix, it in six.moves.zip(x, t):
          loss += self._forward_cpu_one(ix, it, W)
      return (numpy.array(loss),)
  
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 4 for calling the BinaryHierarchicalSoftmaxFunction obj's underlying member method code with example usage: obj.forward_cpu(inputs) and returns: numpy