code: def testMultiDimensionalOutput(
      self, num_microbatches, output_dimension, fast_clipping
  ):
      train_data = np.array(
          [[2.0, 3.0], [4.0, 5.0], [6.0, 7.0], [8.0, 9.0]]
      )
      train_labels = np.array([[0], [1], [1], [0]])
      learning_rate = 1.0
      optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate)
      loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
          from_logits=True
      )
  
      model = dp_keras_model.DPSequential(
          l2_norm_clip=1.0e9,
          noise_multiplier=0.0,
          num_microbatches=num_microbatches,
          layer_registry=layer_registry.make_default_layer_registry()
          if fast_clipping
          else None,
          layers=[
              tf.keras.layers.InputLayer(input_shape=(2,)),
              tf.keras.layers.Dense(
                  output_dimension,
                  use_bias=False,
                  kernel_initializer="zeros",
              ),
              tf.keras.layers.Dense(1),
          ],
      )
      model.compile(optimizer=optimizer, loss=loss_fn)
      model.fit(
          train_data,
          train_labels,
          epochs=1,
          batch_size=4,
          shuffle=False,
      )
  
file_path: privacy/tensorflow_privacy/privacy/keras_models/dp_keras_model_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_privacy.privacy.fast_gradient_clipping import layer_registry
  from tensorflow_privacy.privacy.keras_models import dp_keras_model
label_desc: Use image node_id 6 for calling the DPKerasModelTest obj's underlying member method code with example usage: obj.testMultiDimensionalOutput(num_microbatches, output_dimension, fast_clipping) without return types