code: def testConv3DWeightNotPrunedWithSparsityMbyN(self):
      layer = keras.layers.Conv3D(2, 3)
      inputs = keras.layers.Input(shape=(4, 28, 28, 28, 1))
      _ = layer(inputs)
      self.model.add(Prune(layer, sparsity_m_by_n=(2, 4)))
  
      pruned_layers = pruning_wrapper.collect_prunable_layers(
          self.model
      )
  
      self.assertLen(pruned_layers, 1)
      # Only rank-2 (e.g, Conv2D) or rank-4 (e.g, Dense) weight are pruned with
      # M-by-N sparsity.
      self.assertLen(pruned_layers[0].pruning_vars, 0)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_schedule
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper
label_desc: Use image node_id 11 for calling the PruningWrapperTest obj's underlying member method code with example usage: obj.testConv3DWeightNotPrunedWithSparsityMbyN() without return types