code: def _pruned_model_setup(self, custom_training_loop=False):
      pruned_model = prune.prune_low_magnitude(
          keras_test_utils.build_simple_dense_model()
      )
  
      x_train = np.random.rand(self._BATCH_SIZE, 10)
      y_train = keras.utils.to_categorical(
          np.random.randint(5, size=(self._BATCH_SIZE, 1)), 5
      )
  
      loss = keras.losses.categorical_crossentropy
      optimizer = keras.optimizers.SGD()
  
      if custom_training_loop:
          return pruned_model, loss, optimizer, x_train, y_train
      else:
          pruned_model.compile(
              loss=loss, optimizer=optimizer, metrics=["accuracy"]
          )
          return pruned_model, x_train, y_train
  
file_path: model-optimization/tensorflow_model_optimization/python/core/sparsity/keras/pruning_callbacks_test.py
imports: import os
  import tempfile
  import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.keras import test_utils
  from tensorflow_model_optimization.python.core.sparsity.keras import prune
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_callbacks
label_desc: Use image node_id 2 for calling the PruneCallbacksTest obj's underlying member method code with example usage: obj._pruned_model_setup(custom_training_loop) and returns: pruned_model,  loss,  optimizer,  x_train,  y_train, pruned_model,  x_train,  y_train