}
      TF_RETURN_IF_ERROR(
          ConvertAttribute(elt_type, remove_ref_type, &attr_val));
      list->add_type(attr_val.type());
    } else if (auto attr = mlir::dyn_cast<mlir::TF::ShapeAttr>(a)) {
      AttrValue attr_val;
      TF_RETURN_IF_ERROR(ConvertAttribute(attr, &attr_val));
      *list->add_shape() = attr_val.shape();
    } else if (auto attr = mlir::dyn_cast<mlir::ArrayAttr>(a)) {

    # Convolution ouside the while op is quantized.
    self.assertTrue(
        self._contains_op(
            output_graphdef,
            op_name='XlaConvV2',
            attr_name='RhsT',
            attr_val=attr_value_pb2.AttrValue(type=types_pb2.DT_INT8),
        )
    )
    # TODO: b/294783597 - [Converter][TF-Quantizer] Support quantization for the
    # ops in the while op body for both SRQ and WO

string AttrView::DefaultValue() const {
  const AttrValue &attr_value = attr_.default_value();
  switch (attr_value.value_case()) {
    case AttrValue::VALUE_NOT_SET:
      return "";
    case AttrValue::kType:
      return DataType_Name(attr_value.type());
    case AttrValue::kS:
      return "\"" + attr_value.s() + "\"";
    case AttrValue::kI:
      return std::to_string(attr_value.i());
    case AttrValue::kF:

// CHECK-NEXT: %[[matmul:.*]] = "tf.MatMul"(%arg0, %arg1)
// CHECK-SAME: attr_map = "0:transpose_a,1:transpose_b"
// CHECK-NEXT: tf.BiasAdd
// CHECK-NEXT: tf.Relu6
// CHECK-NEXT: return

// CHECK-LABEL: private @composite_matmul_with_bias_and_relu_fn_1
// CHECK-NEXT: tf.MatMul"(%arg0, %arg1)
// CHECK-SAME: attr_map = "0:transpose_a,1:transpose_b"
// CHECK-NEXT: tf.BiasAdd
// CHECK-NEXT: tf.Relu

    }
  }
  return true;
}

bool HasSymbolRefAttr(Operation* op) {
  for (const auto& attr : op->getAttrs()) {
    Attribute attr_value = attr.getValue();
    if (mlir::isa<SymbolRefAttr>(attr_value)) {
      return true;
    } else if (auto array_attr = mlir::dyn_cast<ArrayAttr>(attr_value)) {
      if (!array_attr.empty() &&
          mlir::isa<SymbolRefAttr>(*array_attr.begin())) {
        return true;
      }
    }

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

  func.func @composite_conv2d_with_relu6_fn(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {

  %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 = ""} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x2x2x2xf32>

void TFE_OpSetAttrValueProto(const TFE_Op* op, const char* attr_name,
                             const void* proto, size_t proto_len,
                             TF_Status* status) {
  tensorflow::AttrValue attr_value;
  if (!attr_value.ParseFromArray(proto, proto_len)) {
    status->status =
        tensorflow::errors::InvalidArgument("Unparseable AttrValue proto");
    return;
  }
  if (op == nullptr) {

#include "tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_statistics_collector_min_max.h"
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/op_requires.h"
#include "tensorflow/core/framework/tensor.h"

      func.emitError()
          << "quant-stablehlo-lift-quantizable-spots-as-functions failed.";
      signalPassFailure();
    }
  }

  // Remove all attr_map attributes.
  module_op.walk([](Operation* op) { op->removeAttr(kAttrMapAttribute); });

  // Perform selective quantization. Iterates over the quantization specs and