code: def prune_low_magnitude(
      to_prune,
      pruning_schedule=pruning_sched.ConstantSparsity(0.5, 0),
      block_size=(1, 1),
      block_pooling_type="AVG",
      pruning_policy=None,
      sparsity_m_by_n=None,
      **kwargs,
  ):
      """Modify a tf.keras layer or model to be pruned during training.
  
      This function wraps a tf.keras model or layer with pruning functionality which
      sparsifies the layer's weights during training. For example, using this with
      50% sparsity will ensure that 50% of the layer's weights are zero.
  
      The function accepts either a single keras layer
      (subclass of `tf.keras.layers.Layer`), list of keras layers or a Sequential
      or Functional tf.keras model and handles them appropriately.
  
      If it encounters a layer it does not know how to handle, it will throw an
      error. While pruning an entire model, even a single unknown layer would lead
      to an error.
  
      Prune a model:
  
      ```python
      pruning_params = {
          'pruning_schedule': ConstantSparsity(0.5, 0),
          'block_size': (1, 1),
          'block_pooling_type': 'AVG'
      }
  
      model = prune_low_magnitude(
          keras.Sequential([
              layers.Dense(10, activation='relu', input_shape=(100,)),
              layers.Dense(2, activation='sigmoid')
          ]), **pruning_params)
      ```
  
      Prune a layer:
  
      ```python
      pruning_params = {
          'pruning_schedule': PolynomialDecay(initial_sparsity=0.2,
              final_sparsity=0.8, begin_step=1000, end_step=2000),
          'block_size': (2, 3),
          'block_pooling_type': 'MAX'
      }
  
      model = keras.Sequential([
          layers.Dense(10, activation='relu', input_shape=(100,)),
          prune_low_magnitude(layers.Dense(2, activation='tanh'), **pruning_params)
      ])
      ```
  
      Pretrained models: you must first load the weights and then apply the
      prune API:
  
      ```python
      model.load_weights(...)
      model = prune_low_magnitude(model)
      ```
  
      Optimizer: this function removes the optimizer. The user is expected to
      compile the model
      again. It's easiest to rely on the default (step starts at 0) and then
      use that to determine the desired begin_step for the pruning_schedules.
  
      Checkpointing: checkpointing should include the optimizer, not just the
      weights. Pruning supports
      checkpointing though
      upon inspection, the weights of checkpoints are not sparse
      (https://github.com/tensorflow/model-optimization/issues/206).
  
      Arguments:
          to_prune: A single keras layer, list of keras layers, or a
            `tf.keras.Model` instance.
          pruning_schedule: A `PruningSchedule` object that controls pruning rate
            throughout training.
          block_size: (optional) The dimensions (height, weight) for the block
            sparse pattern in rank-2 weight tensors.
          block_pooling_type: (optional) The function to use to pool weights in the
            block. Must be 'AVG' or 'MAX'.
          pruning_policy: (optional) The object that controls to which layers
            `PruneLowMagnitude` wrapper will be applied. This API is experimental
            and is subject to change.
          sparsity_m_by_n: default None, otherwise a tuple of 2 integers, indicates
            pruning with m_by_n sparsity, e.g., (2, 4): 2 zeros out of 4 consecutive
            elements. It check whether we can do pruning with m_by_n sparsity.
            If this type of sparsity is not applicable, then an error is thrown.
          **kwargs: Additional keyword arguments to be passed to the keras layer.
            Ignored when to_prune is not a keras layer.
  
      Returns:
        Layer or model modified with pruning wrappers. Optimizer is removed.
  
      Raises:
        ValueError: if the keras layer is unsupported, or the keras model contains
        an unsupported layer.
      """
  
      def _prune_list(layers, **params):
          wrapped_layers = []
  
          for layer in layers:
              # Allow layer that is already wrapped by the pruning wrapper
              # to be used as is.
              # No need to wrap the input layer either.
              if isinstance(layer, pruning_wrapper.PruneLowMagnitude):
                  wrapped_layers.append(layer)
              elif isinstance(layer, keras.layers.InputLayer):
                  # TODO(yunluli): Replace with a clone function in keras.
                  wrapped_layers.append(
                      layer.__class__.from_config(layer.get_config())
                  )
              else:
                  wrapped_layers.append(
                      pruning_wrapper.PruneLowMagnitude(layer, **params)
                  )
  
          return wrapped_layers
  
      def _add_pruning_wrapper(layer):
          if isinstance(layer, keras.Model):
              # Check whether the model is a subclass model.
              if not layer._is_graph_network and not isinstance(
                  layer, keras.models.Sequential
              ):
                  raise ValueError(
                      "Subclassed models are not supported currently."
                  )
  
              return keras.models.clone_model(
                  layer,
                  input_tensors=None,
                  clone_function=_add_pruning_wrapper,
              )
          if isinstance(layer, pruning_wrapper.PruneLowMagnitude):
              return layer
          if pruning_policy and not pruning_policy.allow_pruning(layer):
              return layer
          else:
              return pruning_wrapper.PruneLowMagnitude(layer, **params)
  
      params = {
          "pruning_schedule": pruning_schedule,
          "block_size": block_size,
          "block_pooling_type": block_pooling_type,
          "sparsity_m_by_n": sparsity_m_by_n,
      }
  
      is_sequential_or_functional = isinstance(
          to_prune, keras.Model
      ) and (
          isinstance(to_prune, keras.Sequential)
          or to_prune._is_graph_network
      )
  
      # A subclassed model is also a subclass of keras.layers.Layer.
      is_keras_layer = isinstance(
          to_prune, keras.layers.Layer
      ) and not isinstance(to_prune, keras.Model)
  
      if isinstance(to_prune, list):
          return _prune_list(to_prune, **params)
      elif is_sequential_or_functional:
          if pruning_policy:
              pruning_policy.ensure_model_supports_pruning(to_prune)
          return _add_pruning_wrapper(to_prune)
      elif is_keras_layer:
          params.update(kwargs)
          return pruning_wrapper.PruneLowMagnitude(to_prune, **params)
      else:
          raise ValueError(
              "`prune_low_magnitude` can only prune an object of the following "
              "types: tf.keras.models.Sequential, tf.keras functional model, "
              "tf.keras.layers.Layer, list of tf.keras.layers.Layer. You passed "
              "an object of type: {input}.".format(
                  input=to_prune.__class__.__name__
              )
          )
  
imports: import tensorflow
  from tensorflow_model_optimization.python.core.keras import metrics
  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 2 for calling a global function with example usage: prune_low_magnitude(to_prune, pruning_schedule, block_size, block_pooling_type, pruning_policy, sparsity_m_by_n) and returns: wrapped_layers, _prune_list, keras, layer, layer, pruning_wrapper, _add_pruning_wrapper, pruning_wrapper