class_docstr: Tests correctness of Sigmoid with `low` and `high` parameters set.
class_docstr_tok: ["Tests","correctness","of","Sigmoid","with","`","low","`","and","`","high","`","parameters","set","."]
code: def testNumericallySuperiorToEquivalentChain(self):
      x = np.array([-5.0, 3.0, 17.0, 23.0]).astype(np.float32)
      low = -0.08587775
      high = 0.12498104
  
      bijector = sigmoid.Sigmoid(low=low, high=high, validate_args=True)
  
      equivalent_bijector = chain.Chain(
          [
              shift.Shift(shift=low),
              scale.Scale(scale=high - low),
              sigmoid.Sigmoid(),
          ]
      )
  
      self.assertAllLessEqual(self.evaluate(bijector.forward(x)), high)
  
      # The mathematically equivalent `Chain` bijector can return values greater
      # than the intended upper bound of `high`.
      self.assertTrue(
          (self.evaluate(equivalent_bijector.forward(x)) > high).any()
      )
  
imports: from absl.testing import parameterized
  import numpy
  from scipy import special
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 2 for calling the ShiftedScaledSigmoidBijectorTest obj's underlying member method code with example usage: obj.testNumericallySuperiorToEquivalentChain() without return types