}
      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)) {

    if (attribute_getter_map.find(attr_name.str()) !=
        attribute_getter_map.end()) {
      auto attr_val =
          (attribute_getter_map[attr_name.str()])(rewriter, identifier_to_attr);
      attrs.push_back(rewriter.getNamedAttr(attr_name, attr_val));
    }
  }

  auto feature_group_cnt_attr = llvm::StringRef("feature_group_count");
  int feature_group_cnt = 1;

}

string PrintAttrValue(const string& op, const AttrValue& attr_value) {
  switch (attr_value.value_case()) {
    case AttrValue::kS:
      return PrintString(attr_value.s());
    case AttrValue::kI:
      return strings::StrCat(attr_value.i());
    case AttrValue::kF: {
      const float f = attr_value.f();
      if (std::isinf(f)) {
        return strings::StrCat(f < 0.0f ? "-" : "+",

    %cst_0 = "tf.Const"() {value = dense<2.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32>
    %0 = "tf.MatMul"(%arg0, %cst_0) {attr_map = "0:transpose_a,1:transpose_a", device = "", transpose_a = false, transpose_b = false} : (tensor<1x2x2x2xf32>, tensor<2x1024xf32>) -> tensor<*xf32>

        output_quantization_axis = -1,
        output_quantization_min_val = -2147483648,
        output_quantization_max_val = 2147483647,
        T = "tfdtype$DT_QINT32",
        attr_map = ""
      } : (tensor<*x!tf_type.qint32>, tensor<*x!tf_type.qint32>, tensor<*xf32>, tensor<*xi32>, tensor<*xf32>, tensor<*xi32>, tensor<*xf32>, tensor<*xi32>) -> tensor<*x!tf_type.qint32>

  for (const auto& name_attr_pair : n.attrs()) {
    const AttrValue& attr_value = name_attr_pair.second;
    if (attr_value.value_case() == AttrValue::kFunc) {
      result.push_back(attr_value.func());
    } else if (attr_value.value_case() == AttrValue::kList) {
      result.insert(result.end(), attr_value.list().func().begin(),
                    attr_value.list().func().end());
    }
  }

  return result;
}

    const std::pair<string, bool>& bool_attr) {
  AttrValue attr_value;
  attr_value.set_b(bool_attr.second);
  return {bool_attr.first, attr_value};
}

std::pair<string, AttrValue> impl::AttrLiteralHelper(
    const std::pair<string, absl::Span<const int>>& int_list_attr) {
  AttrValue attr_value;
  AttrValue::ListValue* list = attr_value.mutable_list();
  for (int i : int_list_attr.second) {
    list->add_i(i);

// 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

    func.return %1: tensor<*xf32>
  }
  func.func private @composite_matmul_fn_1(%arg0: tensor<2x12xf32>, %arg1: tensor<12x2xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {
    %0 = "tf.MatMul"(%arg0, %arg1) {attr_map = "0:transpose_a,1:transpose_b", device = "", transpose_a = false, transpose_b = false} : (tensor<2x12xf32>, tensor<12x2xf32>) -> tensor<*xf32>
    return %0 : tensor<*xf32>
  }

// CHECK-LABEL: func @matmul

    func.return %1: tensor<*xf32>
  }
  func.func private @composite_matmul_fn_1(%arg0: tensor<2x12xf32>, %arg1: tensor<12x2xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {
    %0 = "tf.MatMul"(%arg0, %arg1) {attr_map = "0:transpose_a,1:transpose_b", device = "", transpose_a = false, transpose_b = false} : (tensor<2x12xf32>, tensor<12x2xf32>) -> tensor<*xf32>
    return %0 : tensor<*xf32>
  }
}

// PerTensor-LABEL: func @matmul