class_docstr: Tests correctness of the power transformation.
class_docstr_tok: ["Tests","correctness","of","the","power","transformation","."]
code: def testBijectorEndpoints(self, dtype):
      bijector = sinh_arcsinh.SinhArcsinh(
          skewness=dtype(0.0), tailweight=dtype(1.0), validate_args=True
      )
      # Use bounds that are very large to check that the transformation remains
      # bijective. We stray away from the largest/smallest value to avoid issues
      # at the boundary since XLA sinh will return `inf` for the largest value.
      bounds = np.array(
          [
              np.nextafter(np.finfo(dtype).min, 0.0) / 10.0,
              np.nextafter(np.finfo(dtype).max, 0.0) / 10.0,
          ],
          dtype=dtype,
      )
      # Note that the above bijector is the identity bijector. Hence, the
      # log_det_jacobian will be 0. Because of this we use atol.
      bijector_test_util.assert_bijective_and_finite(
          bijector,
          bounds,
          bounds,
          eval_func=self.evaluate,
          event_ndims=0,
          atol=2e-6,
      )
  
file_path: probability/tensorflow_probability/python/bijectors/sinh_arcsinh_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import sinh_arcsinh
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 8 for calling the SinhArcsinhTest obj's underlying member method code with example usage: obj.testBijectorEndpoints(dtype) without return types