code: def testSampleN(self):
      for samples_shape in ([2, 4], [4, 2, 4]):
          input_ = random_samples(samples_shape)
          input_ph = tf1.placeholder_with_default(
              input_, shape=samples_shape if self.static_shape else None
          )
          dist = empirical.Empirical(
              samples=input_ph, event_ndims=1, validate_args=True
          )
          expected_shape = tf.concat(
              [dist.batch_shape_tensor(), dist.event_shape_tensor()],
              axis=0,
          )
          self.assertAllEqual(
              self.evaluate(
                  tf.shape(dist.sample(seed=test_util.test_seed()))
              ),
              expected_shape,
          )
  
          n = 1000
          seed = test_util.test_seed()
          if tf.executing_eagerly():
              tf.random.set_seed(seed)
          samples1 = dist.sample(n, seed)
          if tf.executing_eagerly():
              tf.random.set_seed(seed)
          samples2 = dist.sample(n, seed)
          self.assertAllEqual(
              self.evaluate(samples1), self.evaluate(samples2)
          )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import empirical
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 7 for calling the EmpiricalVectorTest obj's underlying member method code with example usage: obj.testSampleN() without return types