code: def _sparsity_m_by_n_masking(self, weight, m_by_n=(2, 4)):
      mask = tf.Variable(tf.ones(weight.get_shape()), name="mask")
      threshold = tf.Variable(1, name="threshold")
      self.initialize()
  
      # Set up pruning
      p = pruning_impl.Pruning(
          pruning_vars=[(weight, mask, threshold)],
          training_step_fn=self.training_step_fn,
          pruning_schedule=self.constant_sparsity,
          block_size=(1, 1),
          block_pooling_type="AVG",
          sparsity_m_by_n=m_by_n,
      )
  
      _, new_mask = p._maybe_update_block_mask(weight)
  
      return new_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 11 for calling the PruningTest obj's underlying member method code with example usage: obj._sparsity_m_by_n_masking(weight, m_by_n) and returns: new_mask