code: def testBlockMaskingAvg(self):
      block_size = (2, 2)
      block_pooling_type = "AVG"
      weight = tf.constant(
          [
              [0.1, 0.1, 0.2, 0.2],
              [0.1, 0.1, 0.2, 0.2],
              [0.3, 0.3, 0.4, 0.4],
              [0.3, 0.3, 0.4, 0.4],
          ]
      )
      expected_mask = [
          [0.0, 0.0, 0.0, 0.0],
          [0.0, 0.0, 0.0, 0.0],
          [1.0, 1.0, 1.0, 1.0],
          [1.0, 1.0, 1.0, 1.0],
      ]
  
      self._blockMasking(
          block_size, block_pooling_type, weight, expected_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 7 for calling the PruningTest obj's underlying member method code with example usage: obj.testBlockMaskingAvg() without return types