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);

	{"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},

// Checks if the `Method` attatched to the given `tf.XlaCallModule` op has
// `WeightOnlyPtq`.
bool HasWeightOnlyPtqMethod(TF::XlaCallModuleOp xla_call_module_op);

// Checks if an op is a `tf.XlaCallModule` op, contains 'conv' or 'dot_general'
// in its name and has `Method` with `WeightOnlyPtq`.
bool IsWeightOnlyQuantizableOp(const Operation& op);

// Lists the functions in a ModuleOp sorted by their names.

# MLIR:         %[[conv:.*]] = "tfl.conv_2d"(%[[ARG_0]], %[[weight]], %[[bias]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}
# MLIR:         %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[shape]]) : (tensor<1x1x1x186x!quant.uniform<i8:f32, 0.09363494573854933:22>>, tensor<3xi32>)

# MLIR:         %[[conv:.*]] = "tfl.conv_2d"(%[[ARG_0]], %[[weight]], %[[bias]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}
# MLIR:         %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[shape]]) : (tensor<1x1x1x186x!quant.uniform<i8:f32, 0.09363494573854933:22>>, tensor<3xi32>)

// 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).

// 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).

        Convention conv = new ExtensibleDynamicObject(this, DynamicObjectAwareBean.class, TestUtil.instantiatorFactory().decorateLenient()).getConvention();

        public Convention getConvention() {
            return conv;
        }

        public ExtensionContainer getExtensions() {
            return conv;
        }

// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions -quant-quantize-composite-functions | FileCheck %s

module {
  func.func @conv(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>, tensor<*xf32>) {

    StringRef function_name = entry_function.getValue();
    if (!function_name.starts_with("composite_")) {
      return spec;
    }

    if (function_name.contains("conv")) {
      // Looks up `Method` to see if it should be per-channel quantized and
      // populates the spec accordingly.
      PopulateCoeffOpQuantDimIfPerChannelQuantized(call_op, *spec);