code: def testWeibullMode(self):
      batch_size = 25
      concentration = np.array([1.0] * batch_size, dtype=self.dtype)
      scale = np.array([33.0] * batch_size, dtype=self.dtype)
      expected_mode = (
          ((concentration - 1.0) / concentration)
          ** (1.0 / concentration)
      ) * scale
  
      dist = weibull.Weibull(
          concentration=self.make_input(concentration),
          scale=self.make_input(scale),
          validate_args=True,
      )
  
      mode = dist.mode()
      self.assertAllClose(self.evaluate(mode), expected_mode)
      self.assertEqual(
          self.evaluate(dist.batch_shape_tensor()), mode.shape
      )
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import weibull
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 10 for calling the _WeibullTest obj's underlying member method code with example usage: obj.testWeibullMode() without return types