code: def _prune_model(original_model):
      """Apply the pruning wrapper, compile and train the model."""
      prune_epoch = 1
      pruning_params = {
          "pruning_schedule": pruning_schedule.ConstantSparsity(
              0.50, begin_step=0, frequency=10
          )
      }
      pruning_model = prune.prune_low_magnitude(
          original_model, **pruning_params
      )
      callbacks = [pruning_callbacks.UpdatePruningStep()]
      pruning_model = _train_model(
          pruning_model, callbacks, prune_epoch
      )
      pruning_model_stripped = prune.strip_pruning(pruning_model)
  
      return pruning_model, pruning_model_stripped
  
imports: import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.clustering.keras import cluster
  from tensorflow_model_optimization.python.core.clustering.keras import cluster_config
  from tensorflow_model_optimization.python.core.clustering.keras.experimental import cluster
  from tensorflow_model_optimization.python.core.quantization.keras import quantize
  from tensorflow_model_optimization.python.core.quantization.keras.collab_opts.cluster_preserve import cluster_utils
  from tensorflow_model_optimization.python.core.quantization.keras.collab_opts.cluster_preserve import default_8bit_cluster_preserve_quantize_scheme
  from tensorflow_model_optimization.python.core.sparsity.keras import prune
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_callbacks
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_schedule
label_desc: Use image node_id 5 for calling a global function with example usage: _prune_model(original_model) and returns: pruning_model,  pruning_model_stripped