code: def test_depthwise_conv(self, cpu_only=False):
      if not cpu_only:
          pytest.xfail(
              "rdar://116060011: re-activate coremltools tests blocked by Core ML regressions"
          )
  
      def get_coreml_model_depthwise(X, params, w):
          eval = True
          mlmodel = None
          try:
              input_dim = X.shape[2:]
              input_features = [("data", datatypes.Array(*input_dim))]
              output_features = [("output", None)]
              builder = neural_network.NeuralNetworkBuilder(
                  input_features, output_features
              )
              # translate weights : (Kh, Kw, kernel_channels, output_channels) == (Kh, Kw, Cin/g, Cout) == (Kh, Kw, 1, channel_multiplier * Cin)
              w_e = np.reshape(
                  w,
                  (
                      params["kernel_size"],
                      params["kernel_size"],
                      params["multiplier"] * params["C"],
                      1,
                  ),
              )
              w_e = np.transpose(w_e, [0, 1, 3, 2])
              if params["padding"] == "SAME":
                  pad_mode = "same"
              else:
                  pad_mode = "valid"
              builder.add_convolution(
                  "conv",
                  kernel_channels=1,
                  output_channels=params["multiplier"] * params["C"],
                  height=params["kernel_size"],
                  width=params["kernel_size"],
                  stride_height=params["stride"],
                  stride_width=params["stride"],
                  border_mode=pad_mode,
                  groups=params["C"],
                  W=w_e,
                  b=None,
                  has_bias=0,
                  is_deconv=0,
                  output_shape=None,
                  input_name="data",
                  output_name="output",
              )
  
              if cpu_only:
                  compute_unit = ComputeUnit.CPU_ONLY
              else:
                  compute_unit = ComputeUnit.ALL
              mlmodel = MLModel(
                  builder.spec, compute_units=compute_unit
              )
          except RuntimeError as e:
              print(e)
              eval = False
          return mlmodel, eval
  
      def get_tf_predictions_depthwise(X, params, w):
          Cin = params["C"]
          Kh = Kw = params["kernel_size"]
          channel_multiplier = params["multiplier"]
          y = tf.nn.depthwise_conv2d(
              X,
              w,
              strides=[1, params["stride"], params["stride"], 1],
              padding=params["padding"],
          )
          return y.numpy()
  
      """
          Define Params
          """
      params_dict = dict(
          C=[1, 4, 7],
          H=[11, 16],
          stride=[1, 2, 3],
          kernel_size=[1, 2, 3, 5],
          multiplier=[1, 2, 3, 4],
          padding=["SAME", "VALID"],
      )
      params = [
          x for x in list(itertools.product(*params_dict.values()))
      ]
      all_candidates = [
          dict(zip(params_dict.keys(), x)) for x in params
      ]
      valid_params = []
      for pr in all_candidates:
          if pr["padding"] == "VALID":
              if (
                  np.floor((pr["H"] - pr["kernel_size"]) / pr["stride"])
                  + 1
                  <= 0
              ):
                  continue
          valid_params.append(pr)
      print(
          "Total params to be tested: ",
          len(valid_params),
          "out of candidates: ",
          len(all_candidates),
      )
      """
          Test
          """
      failed_tests_compile = []
      for i in range(len(valid_params)):
          params = valid_params[i]
          # print("=========: ", params)
          # if i % 10 == 0: print "======== Testing {}/{}".format(str(i), str(len(valid_params)))
          X = np.random.rand(1, params["C"], params["H"], params["H"])
          w = np.random.rand(
              params["kernel_size"],
              params["kernel_size"],
              params["C"],
              params["multiplier"],
          )
          tf_preds = get_tf_predictions_depthwise(
              np.transpose(X, [0, 2, 3, 1]), params, w
          )
          tf_preds = np.transpose(tf_preds, [0, 3, 1, 2])
          coreml_model, eval = get_coreml_model_depthwise(
              np.expand_dims(X, axis=0), params, w
          )
          if eval is False:
              failed_tests_compile.append(params)
          else:
              input_dict = {"data": np.expand_dims(X, axis=0)}
              ref_output_dict = {"output": tf_preds[0, :, :, :]}
              self._test_model(
                  input_dict, ref_output_dict, coreml_model
              )
  
      self.assertEqual(failed_tests_compile, [])
  
imports: import itertools
  import unittest
  import numpy
  import pytest
  import coremltools.models.datatypes
  from coremltools import ComputeUnit
  from coremltools._deps import _HAS_TF_2, MSG_TF2_NOT_FOUND
  from coremltools.models import MLModel, neural_network
  from coremltools.models.utils import _is_macos, _macos_version
label_desc: Use image node_id 3 for calling the StressTest obj's underlying member method code with example usage: obj.test_depthwise_conv(cpu_only) and returns: mlmodel,  eval, y