%1 = "quantfork.stats"(%arg0) {layerStats = dense<[1.27501142, 149.824783]> : tensor<2xf32>} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xf32>

// Checks if the value has only one user.
def HasOneUse : Constraint<CPred<"$0.hasOneUse()">>;

// If we see a Conv2D op followed by Mul, then multiply the filter
// with the value in Mul.
def FuseMulAndConv2D :
  Pat<(TF_MulOp:$mul (TF_Conv2DOp:$conv $input,
                          (Arith_ConstantOp:$filter F32ElementsAttr:$filter_value),

  %0 = "tf.Conv2D"(%output, %cst) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 2, 2, 1], use_cudnn_on_gpu = true}> {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", device...

    %3 = "tf.Cast"(%2) {Truncate = false} : (tensor<960x960x3x512xi8>) -> tensor<960x960x3x512xi32>
    %4 = "tf.Sub"(%3, %arg5) : (tensor<960x960x3x512xi32>, tensor<512xi32>) -> tensor<960x960x3x512xi32>
    %5 = "tf.Conv2D"(%1, %4) {dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x2240x2240x3xi32>, tensor<960x960x3x512xi32>) -> tensor<1x2240x1120x512xi32>

// Configure `QuantizationSpecs` to apply `StaticRangePtq` to compute heavy
// units.
constexpr absl::string_view kSpecsStaticRangePtqToComputeHeavy =
    R"pb(specs
         [ {
           matcher { function_name { regex: "^.*(conv|dot|gather).*" } }
           method { static_range_ptq {} }
         }])pb";

class TestLiftQuantizableSpotsAsFunctionsWithQuantizationSpecsPass
    : public impl::

      if (function_name.contains("with_bias")) {
        spec->biases_params[2] = {{0, 1},
                                  quant::GetUniformQuantizedTypeForBias};
      }
    } else if (function_name.contains("conv2d")) {
      spec->coeff_op_quant_dim[1] = 3;
      if (function_name.contains("with_bias")) {
        spec->biases_params[2] = {{0, 1},
                                  quant::GetUniformQuantizedTypeForBias};
      }

  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':

	{"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},