code: def test_iO16_default_fp16_multiple_input(
      self,
      float32_two_input_model,
      int32_two_input_model,
      int32_float32_two_input_model,
  ):
      # Case 1: fp32 two inputs model. First input dtype missing
      mlmodel = ct.convert(
          float32_two_input_model,
          inputs=[
              ct.TensorType(name="input1", shape=(10, 20)),
              ct.TensorType(
                  name="input2", shape=(10, 20), dtype=np.float32
              ),
          ],
          minimum_deployment_target=ct.target.iOS16,
      )
      assert_input_dtype(mlmodel, expected_type_str="fp16", index=0)
      assert_input_dtype(mlmodel, expected_type_str="fp32", index=1)
      verify_prediction(mlmodel)
  
      # Case 2: fp32 two inputs model. Second input dtype missing
      mlmodel = ct.convert(
          float32_two_input_model,
          inputs=[
              ct.TensorType(
                  name="input1", shape=(10, 20), dtype=np.float32
              ),
              ct.TensorType(name="input2", shape=(10, 20)),
          ],
          minimum_deployment_target=ct.target.iOS16,
      )
      assert_input_dtype(mlmodel, expected_type_str="fp32", index=0)
      assert_input_dtype(mlmodel, expected_type_str="fp16", index=1)
      verify_prediction(mlmodel)
  
      # Case 3: fp32 two inputs model. Both dtype missing
      mlmodel = ct.convert(
          float32_two_input_model,
          inputs=[
              ct.TensorType(name="input1", shape=(10, 20)),
              ct.TensorType(name="input2", shape=(10, 20)),
          ],
          minimum_deployment_target=ct.target.iOS16,
      )
      assert_input_dtype(mlmodel, expected_type_str="fp16", index=0)
      assert_input_dtype(mlmodel, expected_type_str="fp16", index=1)
      verify_prediction(mlmodel)
  
      # Case 4: fp32 two inputs model. inputs not given
      mlmodel = ct.convert(
          float32_two_input_model,
          minimum_deployment_target=ct.target.iOS16,
      )
      assert_input_dtype(mlmodel, expected_type_str="fp16", index=0)
      assert_input_dtype(mlmodel, expected_type_str="fp16", index=1)
      verify_prediction(mlmodel)
  
      # Case 5: fp32 two inputs model. Both dtype given
      mlmodel = ct.convert(
          float32_two_input_model,
          inputs=[
              ct.TensorType(
                  name="input1", shape=(10, 20), dtype=np.int32
              ),
              ct.TensorType(
                  name="input2", shape=(10, 20), dtype=np.float32
              ),
          ],
          minimum_deployment_target=ct.target.iOS16,
      )
      assert_input_dtype(mlmodel, expected_type_str="int32", index=0)
      assert_input_dtype(mlmodel, expected_type_str="fp32", index=1)
      verify_prediction(mlmodel)
  
      # Case 6: int32 two inputs model. Both dtype missing. No change made.
      mlmodel = ct.convert(
          int32_two_input_model,
          inputs=[
              ct.TensorType(name="input1", shape=(10, 20)),
              ct.TensorType(name="input2", shape=(10, 20)),
          ],
          minimum_deployment_target=ct.target.iOS16,
      )
      assert_input_dtype(mlmodel, expected_type_str="int32", index=0)
      assert_input_dtype(mlmodel, expected_type_str="int32", index=1)
      verify_prediction(mlmodel)
  
      # Case 7: mixed dtype model with two inputs. Both dtype missing. The fp32 input is cast to fp16.
      mlmodel = ct.convert(
          int32_float32_two_input_model,
          inputs=[
              ct.TensorType(name="input1", shape=(10, 20)),
              ct.TensorType(name="input2", shape=(10, 20)),
          ],
          minimum_deployment_target=ct.target.iOS16,
      )
      assert_input_dtype(mlmodel, expected_type_str="int32", index=0)
      assert_input_dtype(mlmodel, expected_type_str="fp16", index=1)
      verify_prediction(mlmodel)
  
imports: import itertools
  import os
  import tempfile
  import numpy
  import pytest
  import coremltools
  from coremltools._deps import _HAS_TF_1, _HAS_TF_2, MSG_TF1_NOT_FOUND
  from coremltools.converters.mil.testing_reqs import backends, compute_units
  from coremltools.converters.mil.testing_utils import assert_cast_ops_count, assert_input_dtype, assert_ops_in_mil_program, assert_output_dtype, assert_prog_input_type, assert_prog_output_type, assert_spec_input_image_type, assert_spec_output_image_type, get_op_types_in_program, verify_prediction
  from coremltools.proto import FeatureTypes_pb2
  from coremltools.test.api.test_api_examples import TestInputs
label_desc: Use image node_id 2 for calling the TestiOS16DefaultIODtype obj's underlying member method code with example usage: obj.test_iO16_default_fp16_multiple_input(float32_two_input_model, int32_two_input_model, int32_float32_two_input_model) without return types