code: def test_pipeline_input_goes_to_multiple_models():
      # Create the first two models that take the same input
      m1 = TestMakePipeline._make_model("x", 20, "y1", 10)
      m2 = TestMakePipeline._make_model("x", 20, "y2", 10)
  
      # Create the last models which add the output from the other two models.
      p3 = Program()
      func_inputs = {
          "y1": mb.placeholder(shape=(10,)),
          "y2": mb.placeholder(shape=(10,)),
      }
      with Function(func_inputs) as ssa_fun:
          y1, y2 = ssa_fun.inputs["y1"], ssa_fun.inputs["y2"]
          y3 = mb.add(x=y1, y=y2, name="y3")
          ssa_fun.set_outputs([y3])
          p3.add_function("main", ssa_fun)
      m3 = ct.convert(p3)
  
      # Get non-pipeline result
      x = np.random.rand(20)
      if _is_macos():
          y1 = m1.predict({"x": x})["y1"]
          y2 = m2.predict({"x": x})["y2"]
          y3 = m3.predict({"y1": y1, "y2": y2})
  
      pipeline_model = ct.utils.make_pipeline(m1, m2, m3)
      if _is_macos():
          y_pipeline = pipeline_model.predict({"x": x})
          assert len(y_pipeline) == 1
          np.testing.assert_allclose(y3["y3"], y_pipeline["y3"])
  
imports: import itertools
  import tempfile
  import unittest
  import numpy
  import pytest
  import coremltools
  from coremltools._deps import _HAS_LIBSVM, _HAS_SKLEARN
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Function, Program
  from coremltools.models.pipeline import PipelineClassifier, PipelineRegressor
  from coremltools.models.utils import _is_macos
label_desc: Use image node_id 6 for calling the TestMakePipeline obj's underlying member method code with example usage: obj.test_pipeline_input_goes_to_multiple_models() without return types