code: def testShapeWithPlaceholders(self):
      if tf.executing_eagerly():
          return
      loc = tf1.placeholder_with_default(5.0, shape=None)
      scale = tf1.placeholder_with_default([1.0, 2], shape=None)
      ub = tf1.placeholder_with_default([10.0, 11.0], shape=None)
      lb = tf1.placeholder_with_default([-1.0], shape=None)
      dist = truncated_normal.TruncatedNormal(
          loc, scale, lb, ub, validate_args=True
      )
  
      self.assertEqual(dist.batch_shape, tf.TensorShape(None))
      self.assertEqual(dist.event_shape, ())
      self.assertAllEqual(self.evaluate(dist.event_shape_tensor()), [])
      self.assertAllEqual(self.evaluate(dist.batch_shape_tensor()), [2])
      self.assertAllEqual(
          self.evaluate(
              dist.sample(5, seed=test_util.test_seed())
          ).shape,
          [5, 2],
      )
  
      ub = tf1.placeholder_with_default([[5.0, 11.0]], shape=None)
      dist = truncated_normal.TruncatedNormal(
          loc, scale, lb, ub, validate_args=True
      )
      self.assertAllEqual(
          self.evaluate(
              dist.sample(5, seed=test_util.test_seed())
          ).shape,
          [5, 1, 2],
      )
  
imports: import itertools
  import unittest
  from absl.testing import parameterized
  import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import truncated_normal
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 4 for calling the TruncatedNormalStandaloneTestCase obj's underlying member method code with example usage: obj.testShapeWithPlaceholders() without return types