code: def test_outputs_are_consistent(
      self, input_packing_type, output_packing_type
  ):
      num_dims = 3
      num_inputs = 1 if input_packing_type is None else 2
      num_outputs = 1 if output_packing_type is None else 2
      sample_inputs = [
          tf.keras.Input((num_dims,)) for _ in range(num_inputs)
      ]
      temp_sum = tf.stack(sample_inputs, axis=0)
      sample_outputs = [
          tf.multiply(temp_sum, float(i + 1.0))
          for i in range(num_outputs)
      ]
      sample_x_batch = [
          tf.multiply(
              tf.range(num_dims, dtype=tf.float32), float(i + 1.0)
          )
          for i in range(num_inputs)
      ]
  
      # Pack inputs.
      if input_packing_type is None:
          inputs = sample_inputs[0]
          x_batch = sample_x_batch[0]
      elif input_packing_type is not dict:
          inputs = input_packing_type(sample_inputs)
          x_batch = input_packing_type(sample_x_batch)
      else:
          inputs = {}
          x_batch = {}
          keys = [str(i) for i in range(len(sample_inputs))]
          for k, v1, v2 in zip(keys, sample_inputs, sample_x_batch):
              inputs[k] = v1
              x_batch[k] = v2
  
      # Pack outputs.
      if output_packing_type is None:
          outputs = sample_outputs[0]
      elif output_packing_type is not dict:
          outputs = output_packing_type(sample_outputs)
      else:
          outputs = {}
          keys = [str(i) for i in range(len(sample_outputs))]
          for k, v in zip(keys, sample_outputs):
              outputs[k] = v
  
      model = tf.keras.Model(inputs=inputs, outputs=outputs)
      computed_outputs, _ = gradient_clipping_utils.model_forward_pass(
          model,
          x_batch,
      )
      true_outputs = model(x_batch)
      self.assertAllClose(computed_outputs, true_outputs)
  
imports: from typing import Any
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_privacy.privacy.fast_gradient_clipping import gradient_clipping_utils
label_desc: Use image node_id 1 for calling the ModelForwardPassTest obj's underlying member method code with example usage: obj.test_outputs_are_consistent(input_packing_type, output_packing_type) without return types