code: def testLogits(self):
      p = np.array([0.2, 0.8], dtype=np.float32)
      logits = np.log(p) - 50.0
      dist = categorical.Categorical(logits=logits, validate_args=True)
      self.assertAllEqual([2], dist.logits.shape)
      self.assertAllClose(logits, self.evaluate(dist.logits))
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.util import deferred_tensor
label_desc: Use image node_id 2 for calling the CategoricalTest obj's underlying member method code with example usage: obj.testLogits() without return types