code: def _testLabelsBroadcast(self, uniform_labels_gradient):
      labels = np.array([[0.0, 0.0, 0.0, 1.0]]).astype(np.float16)
      logits = np.array(
          [[1.0, 1.0, 1.0, 1.0], [1.0, 2.0, 3.0, 4.0]]
      ).astype(np.float16)
      self._testXent2D(labels, logits, with_placeholders=True)
      labels = np.array([[1.0]]).astype(np.float16)
      logits = np.array([[1.0], [2.0]]).astype(np.float16)
      self._testXent2D(labels, logits, with_placeholders=True)
      labels = np.array([[0.0], [2.0], [0.25]]).astype(np.float16)
      logits = np.array(
          [
              [1.0, 1.0, 1.0, 1.0],
              [1.0, 2.0, 3.0, 4.0],
              [1.0, 2.0, 3.0, 4.0],
          ]
      ).astype(np.float16)
      self._testXent2D(
          labels,
          logits,
          with_placeholders=True,
          expected_gradient=uniform_labels_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 8 for calling the XentOpTestBase obj's underlying member method code with example usage: obj._testLabelsBroadcast(uniform_labels_gradient) without return types