code: def testGradientLogits(self):
      t = tf.Variable([0.01, 1.0])
      logits = tf.Variable([[-1.0, 0.0, 1], [3.0, 3.0, 3.0]])
      d = roc.ExpRelaxedOneHotCategorical(
          t, logits=logits, validate_args=True
      )
      with tf.GradientTape() as tape:
          loss = -d.log_prob(
              tf.math.log_softmax([[-1.0, 0.0, 0.0], [0.0, 0.0, 1.0]])
          )
      g = tape.gradient(loss, d.trainable_variables)
      self.assertLen(g, 2)
      self.assertAllNotNone(g)
  
imports: from absl.testing import parameterized
  import numpy
  from scipy.special import gamma
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.distributions import relaxed_onehot_categorical
  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 ExpRelaxedOneHotCategoricalFromVariableTest obj's underlying member method code with example usage: obj.testGradientLogits() without return types