return %2 : tensor<?x2xf32>
    }
    func.func private @composite_fn_1(%arg0: tensor<?x2xf32>, %arg1: tensor<2x2xf32>, %arg2: tensor<2xf32>) -> tensor<?x2xf32> attributes {_from_xla_call_module, tf_quant.composite_function} {
      return %arg0 : tensor<?x2xf32>
    }
  }
)mlir";

constexpr absl::string_view kModuleDotWeightOnlyPtq = R"mlir(
  module {

    return %3 : tensor<?x2xf32>
  }
  func.func private @composite_dot_general_with_bias_and_relu6_dynamic_fn_2(%arg0: tensor<?x2xf32>, %arg1: tensor<2x2xf32>, %arg2: tensor<2xf32>) -> tensor<?x2xf32> attributes {_from_xla_call_module, tf_quant.composite_function} {
    %0 = stablehlo.constant dense<0.000000e+00> : tensor<f32>
    %1 = stablehlo.constant dense<6.000000e+00> : tensor<f32>

      return %1 : tensor<1x4x4x3xf32>
    }
    func.func private @composite_fn_1(%arg0: tensor<1x4x4x3xf32>, %arg1: tensor<3x1x1x3xf32>, %arg2: tensor<3xf32>) -> tensor<1x4x4x3xf32> attributes {tf_quant.composite_function} {

    func.return %1: tensor<*xf32>
  }
  func.func private @composite_depthwise_conv2d_fn(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {
    %0 = "tf.DepthwiseConv2dNative"(%arg0, %arg1) {

namespace tensorflow {
namespace quantization {

// Default MLIR dump file prefix for TensorFlow quantization passes.
inline constexpr absl::string_view kDefaultTfQuantMlirDumpFilePrefix =
    "tf_quant";

// Preprocesses the `module_op` for quantization. The preprocess steps include
// freezing the variables in the graph into constants. `is_inliner_run`
// determines whether the `InlinerPass` should be run after unfreezing.

namespace mlir::quant {

// This attribute will be set for functions created by this pass.
// Presence of this attribute will mark the function as quantization target.
inline constexpr StringRef kFusedFunctionAttr = "tf_quant.composite_function";
// The keyword to detect if this is a `NullAttribute`.
inline constexpr StringRef kNullAttributeValue = "N/A";

// Prefixes attached to lifted functions.

    return %2 : tensor<*xf32>
  }

  func.func private @composite_conv2d_with_bias_and_relu6_fn_10(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>, %arg2: tensor<2xf32>) -> tensor<*xf32> attributes {tf.tf_quant.composite_function} {
    %0 = "quantfork.stats"(%arg1) {layerStats = dense<[-9.54062747, 9.54742622]> : tensor<2xf32>} : (tensor<2x3x3x2xf32>) -> tensor<2x3x3x2xf32>

class NamedAttr<string attr_name> :
    NativeCodeCall<"NamedAttribute{$_builder.getStringAttr(\"" # attr_name # "\"), $0}">;

// Checks if the value is not defined inside a lifted function by checking the
// `tf_quant.composite_function` attribute.
def IsNotInLiftedFunc :
      Constraint<CPred<"!IsInLiftedFunc($0.getDefiningOp())">>;

// Checks if the value is not inside a StableHLO op with region.
def IsNotInStableHloOpRegion :

    func.return %1: tensor<*xf32>
  }
  func.func private @composite_depthwise_conv2d_fn(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {
    %0 = "tf.DepthwiseConv2dNative"(%arg0, %arg1) {

                           %bias_scale : tensor<*xf32>, %bias_zp : tensor<*xi32>,
                           %out_scale : tensor<*xf32>, %out_zp : tensor<*xi32>) -> tensor<*x${output_type}>
        attributes {tf_quant.quantized_ops = ${quantized_ops}} {
      %0 = "tf.PartitionedCall"(%input, %filter, %input_scale, %input_zp,
                                  %filter_scale, %filter_zp) {