class_docstr: Tests the correctness of the Y = Chain(bij1, bij2, bij3) transformation.
code: def testBijectorIdentity(self):
      bijector = chain.Chain()
      self.assertStartsWith(bijector.name, "identity")
      x = np.asarray([[[1.0, 2.0], [2.0, 3.0]]])
      self.assertAllClose(x, self.evaluate(bijector.forward(x)))
      self.assertAllClose(x, self.evaluate(bijector.inverse(x)))
      self.assertAllClose(
          0.0,
          self.evaluate(
              bijector.inverse_log_det_jacobian(x, event_ndims=1)
          ),
      )
      self.assertAllClose(
          0.0,
          self.evaluate(
              bijector.forward_log_det_jacobian(x, event_ndims=1)
          ),
      )
  
imports: import mock
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import inline
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import joint_map
  from tensorflow_probability.python.bijectors import restructure
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import scale_matvec_diag
  from tensorflow_probability.python.bijectors import softmax_centered
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.bijectors import split
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 2 for calling the ChainBijectorTest obj's underlying member method code with example usage: obj.testBijectorIdentity() without return types