code: def test_calculate_branching_model_parameters_output_size_last_two(
      self,
  ):
      left_inputs = [0, 1, 2, 2]
      right_inputs = [0, 1, 2, 2]
      left_output_dims = [1, 10, 100, 1000]
      right_output_dims = [10000, 100000, 1000000, 10000000]
      right_layers = [
          layers.IDENTITY_REGISTRY_KEY,
          layers.STANDARD_CONV_1X1_REGISTRY_KEY,
          layers.STANDARD_CONV_1X1_REGISTRY_KEY,
          layers.IDENTITY_REGISTRY_KEY,
      ]
      combiner_functions = [
          translation_nas_net.ADD_COMBINER_FUNC_KEY,
          translation_nas_net.ADD_COMBINER_FUNC_KEY,
          translation_nas_net.MULTIPLY_COMBINER_FUNC_KEY,
          translation_nas_net.CONCAT_COMBINER_FUNC_KEY,
      ]
  
      (
          num_cells,
          _,
          left_layers,
          _,
          _,
          _,
          _,
          _,
          final_combiner_function,
          dummy_activations,
          dummy_norms,
          layer_registry,
          _,
      ) = _get_transformer_branching_encoder_config()
  
      # Get predicted number of parameters.
      (
          _,
          output_size,
          _,
          _,
      ) = translation_nas_net.calculate_branching_model_parameters(
          encoding_depth=_EMBEDDING_DEPTH,
          left_inputs=left_inputs,
          left_layers=left_layers,
          left_output_dims=left_output_dims,
          right_inputs=right_inputs,
          right_layers=right_layers,
          right_output_dims=right_output_dims,
          combiner_functions=combiner_functions,
          final_combiner_function=final_combiner_function,
          layer_registry=layer_registry,
          num_cells=num_cells,
          encoder_depth=_EMBEDDING_DEPTH,
          enforce_output_size=False,
          enforce_fixed_output_sizes=False,
      )
  
      self.assertEqual(output_size, 11001000)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  from tensor2tensor.data_generators import problem_hparams
  from tensor2tensor.layers import common_attention
  from tensor2tensor.models import transformer
  from tensor2tensor.models.neural_architecture_search import nas_layers
  from tensor2tensor.models.neural_architecture_search import nas_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 9 for calling the NasSeq2SeqTest obj's underlying member method code with example usage: obj.test_calculate_branching_model_parameters_output_size_last_two() without return types