code: def testAutoVectorization(self, bijector_name, data):
      # TODO(b/150161911): reconcile numeric behavior of eager and graph mode.
      if tf.executing_eagerly():
          return
  
      bijector, event_dim = self._draw_bijector(
          bijector_name,
          data,
          batch_shape=[],  # Avoid conflict with vmap sample dimension.
          validate_args=False,  # Work around lack of `If` support in vmap.
          allowed_bijectors=(
              set(bhps.INSTANTIABLE_BIJECTORS)
              - set(AUTOVECTORIZATION_IS_BROKEN)
          ),
      )
      atol = AUTOVECTORIZATION_ATOL[bijector_name]
      rtol = AUTOVECTORIZATION_RTOL[bijector_name]
  
      # Forward
      n = 3
      xs = self._draw_domain_tensor(
          bijector, data, event_dim, sample_shape=[n]
      )
      ys = bijector.forward(xs)
      vectorized_ys = tf.vectorized_map(
          bijector.forward, xs, fallback_to_while_loop=False
      )
      self.assertAllClose(
          *self.evaluate((ys, vectorized_ys)), atol=atol, rtol=rtol
      )
  
      # FLDJ
      event_ndims = data.draw(
          hps.integers(
              min_value=bijector.forward_min_event_ndims,
              max_value=ps.rank_from_shape(xs.shape) - 1,
          )
      )
      fldj_fn = functools.partial(
          bijector.forward_log_det_jacobian, event_ndims=event_ndims
      )
      vectorized_fldj = tf.vectorized_map(
          fldj_fn, xs, fallback_to_while_loop=False
      )
      fldj = tf.broadcast_to(fldj_fn(xs), tf.shape(vectorized_fldj))
      self.assertAllClose(
          *self.evaluate((fldj, vectorized_fldj)), atol=atol, rtol=rtol
      )
  
      # Inverse
      ys = self._draw_codomain_tensor(
          bijector, data, event_dim, sample_shape=[n]
      )
      xs = bijector.inverse(ys)
      vectorized_xs = tf.vectorized_map(
          bijector.inverse, ys, fallback_to_while_loop=False
      )
      self.assertAllClose(
          *self.evaluate((xs, vectorized_xs)), atol=atol, rtol=rtol
      )
  
      # ILDJ
      event_ndims = data.draw(
          hps.integers(
              min_value=bijector.inverse_min_event_ndims,
              max_value=ps.rank_from_shape(ys.shape) - 1,
          )
      )
      ildj_fn = functools.partial(
          bijector.inverse_log_det_jacobian, event_ndims=event_ndims
      )
      vectorized_ildj = tf.vectorized_map(
          ildj_fn, ys, fallback_to_while_loop=False
      )
      ildj = tf.broadcast_to(ildj_fn(ys), tf.shape(vectorized_ildj))
      self.assertAllClose(
          *self.evaluate((ildj, vectorized_ildj)), atol=atol, rtol=rtol
      )
  
imports: import collections
  import functools
  from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python import bijectors
  from tensorflow_probability.python import util
  from tensorflow_probability.python.bijectors import hypothesis_testlib
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.internal import auto_composite_tensor
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow.python.util import nest
label_desc: Use image node_id 6 for calling the BijectorPropertiesTest obj's underlying member method code with example usage: obj.testAutoVectorization(bijector_name, data) without return types