code: def testGradientThroughLocScale(self):
      loc = tf.Variable(1.0)
      scale = tf.Variable(2.5)
      d = gp.GeneralizedPareto(
          loc=loc, scale=scale, concentration=0.15, validate_args=True
      )
      with tf.GradientTape() as tape:
          loss = -d.log_prob([1.0, 2.0, 4.0])
      grads = tape.gradient(loss, d.trainable_variables)
      self.assertLen(grads, 2)
      self.assertAllNotNone(grads)
  
imports: import hypothesis
  import hypothesis.strategies
  import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import generalized_pareto
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 20 for calling the GeneralizedParetoTest obj's underlying member method code with example usage: obj.testGradientThroughLocScale() without return types