class_docstr: Tests correctness of the Y = scale @ x transformation.
class_docstr_tok: ["Tests","correctness","of","the","Y","=","scale","@","x","transformation","."]
code: def testTinyScale(self, dtype):
      log_scale = tf.cast(-2000.0, dtype)
      x = tf.cast(1.0, dtype)
      scale = tf.math.exp(log_scale)
      fldj_linear = scale_lib.Scale(
          scale=scale
      ).forward_log_det_jacobian(x, event_ndims=0)
      fldj_log = scale_lib.Scale(
          log_scale=log_scale
      ).forward_log_det_jacobian(x, event_ndims=0)
      fldj_linear_, fldj_log_ = self.evaluate([fldj_linear, fldj_log])
      # Using the linear scale will saturate to 0, and produce bad log-det
      # Jacobians.
      self.assertNotEqual(fldj_linear_, fldj_log_)
      self.assertAllClose(-2000.0, fldj_log_)
  
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 scale
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 2 for calling the ScaleBijectorTest obj's underlying member method code with example usage: obj.testTinyScale(dtype) without return types