code: def testConstructsMaskAndThresholdCorrectly(self):
      self.initialize()
      p = pruning_impl.Pruning(
          lambda: 0,
          None,
          # Sparsity math often returns values with small tolerances.
          lambda x: (True, 0.200000018),
          (1, 1),
          None,
          False,
      )
  
      # input matrix is [ 1.0, 2.0, ..., 8.0, 9.0, 10.0 ]
      threshold, mask = p._update_mask(np.arange(1, 11))
  
      self.assertEqual(3, K.get_value(threshold))
      self.assertAllEqual(
          # expected matrix is [ 0.0, 0.0, 1.0, 1.0 ... 1.0 ]
          np.concatenate((np.zeros(2), np.ones(8))),
          K.get_value(mask),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.keras import compat
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_impl
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_schedule
label_desc: Use image node_id 5 for calling the PruningTest obj's underlying member method code with example usage: obj.testConstructsMaskAndThresholdCorrectly() without return types