code: def _train_model(
      self, model, epochs=1, x_train=None, y_train=None, callbacks=None
  ):
      if x_train is None:
          x_train = (np.random.rand(20, 10),)
      if y_train is None:
          y_train = keras.utils.to_categorical(
              np.random.randint(5, size=(20, 1)), 5
          )
  
      if model.optimizer is None:
          model.compile(
              loss="categorical_crossentropy",
              optimizer="sgd",
              metrics=["accuracy"],
          )
  
      if callbacks is None:
          callbacks = []
          if self._is_pruned(model):
              callbacks = [pruning_callbacks.UpdatePruningStep()]
  
      model.fit(
          x_train,
          y_train,
          epochs=epochs,
          batch_size=20,
          callbacks=callbacks,
      )
  
imports: import tempfile
  from absl.testing import parameterized
  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 prune_registry
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_callbacks
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_schedule
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper
  from tensorflow_model_optimization.python.core.sparsity.keras import test_utils
label_desc: Use image node_id 6 for calling the PruneIntegrationTest obj's underlying member method code with example usage: obj._train_model(model, epochs, x_train, y_train, callbacks) without return types