class_docstr: Tests the correctness of the Y = Chain(bij1, bij2, bij3) transformation.
code: def testInvalidChainNdimsRaisesError(self):
      with self.assertRaisesRegexp(
          ValueError,
          "Differences between `event_ndims` and `min_event_ndims must be equal",
      ):
          chain.Chain(
              [
                  ShapeChanging([1, 1], [1, 1]),
                  ShapeChanging([1, 1], [2, 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 12 for calling the ChainBijectorTest obj's underlying member method code with example usage: obj.testInvalidChainNdimsRaisesError() without return types