code: def check_backward(self, x_data, t_data, y_grad):
      def f(x, t):
          # Force to cast the dtype of an internal state so that we can
          # perform numerical gradient in higher precision.
          if self.dtype == numpy.float16 and x.dtype == numpy.float64:
              self.link._func.codes = self.link._func.codes.astype(
                  x.dtype
              )
          return self.link(x, t)
  
      gradient_check.check_backward(
          f,
          (x_data, t_data),
          y_grad,
          self.link.W,
          atol=1e-4,
          rtol=1e-3,
          **self.check_backward_options,
      )
  
imports: import copy
  import unittest
  import numpy
  import chainer
  from chainer.backends import cuda
  from chainer import gradient_check
  from chainer import links
  from chainer import testing
  from chainer.testing import attr
  from chainer.testing import condition
label_desc: Use image node_id 7 for calling the TestBinaryHierarchicalSoftmax obj's underlying member method code with example usage: obj.check_backward(x_data, t_data, y_grad) and returns: self