code: def test_simple_loop_fixed_iterations(self):
      input_features = [("data", datatypes.Array(1))]
      output_features = [("output", None)]
  
      builder_top = NeuralNetworkBuilder(
          input_features,
          output_features,
          disable_rank5_shape_mapping=True,
      )
      builder_top.add_copy(
          "copy_1", input_name="data", output_name="output"
      )
  
      loop_layer = builder_top.add_loop("loop_layer")
      loop_layer.loop.maxLoopIterations = 5
      builder_body = NeuralNetworkBuilder(
          input_features=None,
          output_features=None,
          spec=None,
          nn_spec=loop_layer.loop.bodyNetwork,
      )
      builder_body.add_elementwise(
          "add",
          input_names=["output"],
          output_name="x",
          mode="ADD",
          alpha=2,
      )
  
      builder_body.add_copy(
          "copy_2", input_name="x", output_name="output"
      )
      coremltools.models.utils.save_spec(
          builder_top.spec, "/tmp/simple_loop_fixed_iterations.mlmodel"
      )
      mlmodel = MLModel(builder_top.spec)
  
      # True branch case
      input_dict = {"data": np.array([0], dtype="float")}
      output_ref = {"output": np.array([10], dtype="float")}
      self._test_model(mlmodel, input_dict, output_ref)
  
imports: import numpy
  import pytest
  import unittest
  import coremltools
  from coremltools import ComputeUnit
  from coremltools.converters.mil.mil.types.type_mapping import np_val_to_py_type
  from coremltools.models import MLModel, datatypes
  from coremltools.models.neural_network import NeuralNetworkBuilder
  from coremltools.models.neural_network.quantization_utils import _convert_array_to_nbit_quantized_bytes, quantize_weights
  from coremltools.models.utils import _is_macos, _macos_version
label_desc: Use image node_id 5 for calling the ControlFlowCorrectnessTest obj's underlying member method code with example usage: obj.test_simple_loop_fixed_iterations() without return types