code: def testInvertMutuallyConsistent(self, layer_cls):
      # BatchNorm bijector is only mutually consistent when training=False.
      dims = 4
      training = tf1.placeholder_with_default(False, (), "training")
      layer = layer_cls(epsilon=0.0)
      batch_norm = invert.Invert(
          batch_normalization.BatchNormalization(
              batchnorm_layer=layer, training=training
          )
      )
      dist = transformed_distribution.TransformedDistribution(
          distribution=sample.Sample(normal.Normal(0.0, 1.0), [dims]),
          bijector=batch_norm,
          validate_args=True,
      )
      self.run_test_sample_consistent_log_prob(
          sess_run_fn=self.evaluate,
          dist=dist,
          num_samples=int(1e5),
          radius=2.0,
          center=0.0,
          rtol=0.02,
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import batch_normalization
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import reshape
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
label_desc: Use image node_id 5 for calling the BatchNormTest obj's underlying member method code with example usage: obj.testInvertMutuallyConsistent(layer_cls) without return types