code: def testGradientLogits(self):
      x = tf.Variable([-1.0, 0.0, 1])
      d = oc.OneHotCategorical(logits=x, validate_args=True)
      with tf.GradientTape() as tape:
          loss = -d.log_prob([[1, 0, 0], [0, 0, 1]])
      g = tape.gradient(loss, d.trainable_variables)
      self.assertLen(g, 1)
      self.assertAllNotNone(g)
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import onehot_categorical
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.util import deferred_tensor
label_desc: Use image node_id 1 for calling the OneHotCategoricalFromVariableTest obj's underlying member method code with example usage: obj.testGradientLogits() without return types