return spec;
}

QuantizationSpec GetDefaultWeightOnlyPtqSpec() {
  QuantizationSpec spec{};
  spec.mutable_matcher()->mutable_function_name()->set_regex(
      "^.*(conv|dot_general).*");

  WeightOnlyPtq& weight_only_ptq_spec =
      *spec.mutable_method()->mutable_weight_only_ptq();
  if (auto [iter, inserted] =
          weight_only_ptq_spec.mutable_input_quantized_types()->try_emplace(1);

        when:
        def base = new DefaultProjectDependency(project, "conf1", true, TestFiles.taskDependencyFactory())
        def differentConf = new DefaultProjectDependency(project, "conf2", true, TestFiles.taskDependencyFactory())
        def differentBuildDeps = new DefaultProjectDependency(project, "conf1", false, TestFiles.taskDependencyFactory())

    derived_attrs=['T: {float, int8}'],
    outputs=['o: T'])
def _composite_conv_add_relu(input_, filter_, bias, stride_w, stride_h,
                             dilation_w, dilation_h, padding, act):
  res = tf.raw_ops.Conv2D(
      input=input_,
      filter=filter_,
      strides=[1, stride_w, stride_h, 1],
      dilations=[1, dilation_w, dilation_h, 1],
      padding=padding)
  res = tf.raw_ops.Add(x=res, y=bias)
  if act == 'RELU':

  %input_scale = "tf.Const"() {value = dense<1.0> : tensor<f32>} : () -> tensor<f32>
  %side_input_scale = "tf.Const"() {value = dense<2.0> : tensor<f32>} : () -> tensor<f32>
  %conv2d = "tf._FusedConv2D"(%input, %filter, %bias, %act, %input_scale, %side_input_scale) {

	{"runtime.slicerunetostring", 1},
	{"runtime.stringtoslicebyte", 1},
	{"runtime.stringtoslicerune", 1},
	{"runtime.slicecopy", 1},
	{"runtime.decoderune", 1},
	{"runtime.countrunes", 1},
	{"runtime.convT", 1},
	{"runtime.convTnoptr", 1},
	{"runtime.convT16", 1},
	{"runtime.convT32", 1},
	{"runtime.convT64", 1},
	{"runtime.convTstring", 1},
	{"runtime.convTslice", 1},
	{"runtime.assertE2I", 1},

      %outputs_6, %control_7 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<[-1, 16384]> : tensor<2xi32>} : () -> tensor<2xi32>
      %outputs_8, %control_9 = tf_executor.island wraps "tf.Conv2D"(%arg0, %outputs_0) {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 2, 2, 1], use_cudnn_on_gpu = true} : (tensor<16x224x224x3xf32>, tensor<*xf32>) -> tensor<16x112x112x?xf32>

                    <conf name="conf2" />
                </configurations>
                <publications defaultconf="conf2">
                    <artifact/>
                </publications>
            </ivy-module>
        """

        when:
        parse(parseContext, file)

        then:
        artifact().configurations == ["conf2"] as Set
    }

    %clamp_min = "tf.Minimum"(%clamp_max, %clip_max) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

    func.return %clamp_min : tensor<*xf32>
  }

  // Conv2D with int32 accumulation.
  func.func private @internal_conv2d_fn(
                         %input : tensor<*xi8>, %filter : tensor<*xi8>,
                         %input_scale : tensor<*xf32>, %input_zp : tensor<*xi32>,

// issue 7205

var i9 interface{}

func f9() bool {
	g8()
	x := i9
	y := interface{}(g18()) // ERROR "live at call to convT: x.data$" "live at call to g18: x.data$" "stack object .autotmp_[0-9]+ \[2\]string$"
	i9 = y                  // make y escape so the line above has to call convT
	return x != y
}

// liveness formerly confused by UNDEF followed by RET,
// leading to "live at entry to f10: ~r1" (unnamed result).

// func.func func_name_${key1}_fn (...) {
//   ...${key2}...
// }
// ```
// The above template with generate two functions by substituting `key1` and
// `key2` with given values.

module {

  for main_op in ["Conv2D", "DepthwiseConv2D", "MatMul"] {
    parameters[
      {"quantized_ops": ["${main_op}", "BiasAdd"], "act_func": "internal_requantize_no_activation_fn", "output_type": "!tf_type.qint8"},