class_docstr: Tests pg losses.
      
code: def setUp(self):
      tf.test.TestCase.setUp(self)
      self._batch_size = 64
      self._max_time = 16
      self._d1 = 32
      self._d2 = 32
      self._d3 = 32
      self._num_classes = 10
      self._actions_batch = tf.ones(
          [
              self._batch_size,
              self._max_time,
              self._d1,
              self._d2,
              self._d3,
          ],
          dtype=tf.int32,
      )
      self._logits_batch = tf.random_uniform(
          [
              self._batch_size,
              self._max_time,
              self._d1,
              self._d2,
              self._d3,
              self._num_classes,
          ]
      )
      self._advantages_batch = tf.random_uniform(
          [
              self._batch_size,
              self._max_time,
              self._d1,
              self._d2,
              self._d3,
          ]
      )
      self._actions_no_batch = tf.ones(
          [self._max_time, self._d1, self._d2, self._d3], dtype=tf.int32
      )
      self._logits_no_batch = tf.random_uniform(
          [
              self._max_time,
              self._d1,
              self._d2,
              self._d3,
              self._num_classes,
          ]
      )
      self._advantages_no_batch = tf.random_uniform(
          [self._max_time, self._d1, self._d2, self._d3]
      )
      self._sequence_length = tf.random_uniform(
          [self._batch_size], maxval=self._max_time, dtype=tf.int32
      )
  
imports: import tensorflow
  import texar.tf
label_desc: Use image node_id 1 for calling the PGLossesTest obj's underlying member method code with example usage: obj.setUp() without return types