code: def testUpdateSingleMask(self):
      weight = tf.Variable(np.linspace(1.0, 100.0, 100), name="weights")
      weight_dtype = weight.dtype.base_dtype
      mask = tf.Variable(
          tf.ones(weight.get_shape(), dtype=weight_dtype),
          name="mask",
          dtype=weight_dtype,
      )
      threshold = tf.Variable(
          tf.zeros([], dtype=weight_dtype),
          name="threshold",
          dtype=weight_dtype,
      )
      self.initialize()
  
      p = pruning_impl.Pruning(
          pruning_vars=[(weight, mask, threshold)],
          training_step_fn=self.training_step_fn,
          pruning_schedule=self.constant_sparsity,
          block_size=self.block_size,
          block_pooling_type=self.block_pooling_type,
      )
  
      mask_before_pruning = K.get_value(mask)
      self.assertAllEqual(np.count_nonzero(mask_before_pruning), 100)
  
      if tf.executing_eagerly():
          p.conditional_mask_update()
      else:
          K.get_session().run(p.conditional_mask_update())
  
      mask_after_pruning = K.get_value(mask)
      self.assertAllEqual(np.count_nonzero(mask_after_pruning), 50)
  
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 4 for calling the PruningTest obj's underlying member method code with example usage: obj.testUpdateSingleMask() without return types