code: def _testSingleClass(
      self, expected_gradient=[[2.0], [1.0], [0.0], [0.0]]
  ):
      for dtype in (
          np.float16,
          np.float32,
          dtypes.bfloat16.as_numpy_dtype,
      ):
          loss, gradient = self._opFwdBwd(
              labels=np.array([[-1.0], [0.0], [1.0], [1.0]]).astype(
                  dtype
              ),
              logits=np.array([[1.0], [-1.0], [0.0], [1.0]]).astype(
                  dtype
              ),
          )
          self.assertAllClose([0.0, 0.0, 0.0, 0.0], loss)
          self.assertAllClose(expected_gradient, gradient)
  
imports: import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.framework import config
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops.nn_grad import _SoftmaxCrossEntropyWithLogitsGrad
  from tensorflow.python.platform import test
label_desc: Use image node_id 5 for calling the XentOpTestBase obj's underlying member method code with example usage: obj._testSingleClass(expected_gradient) without return types