code: def test_split_sample_with_sample_shape(
      self, is_static, get_distributions, event_shape
  ):
      _, concat_dist = get_distributions(is_static=is_static)
      x_sample = concat_dist.sample(7, seed=test_util.test_seed())
      sample_shape_size, x_split = concat_dist._split_sample(x_sample)
      self.assertEqual(len(x_split), 3)
      if tf.executing_eagerly() or is_static:
          self.assertEqual(sample_shape_size, 1)
      else:
          self.assertEqual(self.evaluate(sample_shape_size), 1)
  
      self.assertAllEqual(
          self.evaluate(tf.shape(x_split[0])),
          [7, 2, 3, 4] + event_shape,
      )
      self.assertAllEqual(
          self.evaluate(tf.shape(x_split[1])),
          [7, 2, 2, 4] + event_shape,
      )
      self.assertAllEqual(
          self.evaluate(tf.shape(x_split[2])),
          [7, 2, 1, 4] + event_shape,
      )
  
imports: from absl.testing import parameterized
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import batch_concat
  from tensorflow_probability.python.distributions import dirichlet
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import onehot_categorical
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 8 for calling the BatchConcatTest obj's underlying member method code with example usage: obj.test_split_sample_with_sample_shape(is_static, get_distributions, event_shape) without return types