code: def test_selects_batch_members_from_list_of_arrays(self):
      # Shape of each array: [2, 3] = [batch_size, event_size]
      # This test verifies that is_accepted selects batch members, despite the
      # "usual" broadcasting being applied on the right first (event first).
      zeros_states = [np.zeros((2, 3))]
      ones_states = [np.ones((2, 3))]
      chosen = util.choose(
          tf.constant([True, False]), zeros_states, ones_states
      )
      chosen_ = self.evaluate(chosen)
  
      # Make sure outer list wasn't interpreted as a dimenion of an array.
      self.assertIsInstance(chosen_, list)
      expected_array = np.array(
          [
              [0.0, 0.0, 0.0],  # zeros_states selected for first batch
              [1.0, 1.0, 1.0],  # ones_states selected for second
          ]
      )
      expected = [expected_array]
      self.assertAllEqual(expected, chosen_)
  
imports: import collections
  import warnings
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc.internal import util
label_desc: Use image node_id 2 for calling the ChooseTest obj's underlying member method code with example usage: obj.test_selects_batch_members_from_list_of_arrays() without return types