def.add_input("input1");
  def.add_input("input2");
  def.add_input("input3");
  def.add_input("input3");
  def.add_input("input3");

  AttrValue v0;
  v0.set_type(DataType::DT_INT32);
  v0.set_i(3);
  (*def.mutable_attr())["NumInput3"] = v0;
  AttrValue v1;
  v1.set_type(DataType::DT_FLOAT);
  (*def.mutable_attr())["SomeDataTypeAttr"] = v1;

impl::NodeMatcherProperties Attr(std::pair<string, AttrValue> attrs);
impl::NodeMatcherProperties Attr(string name);

std::pair<string, AttrValue> AttrLiteralHelper(
    const std::pair<string, bool>& bool_attr);

std::pair<string, AttrValue> AttrLiteralHelper(
    const std::pair<string, absl::Span<const int>>& int_list_attr);

std::pair<string, AttrValue> AttrLiteralHelper(

  EXPECT_EQ(shape_inference_graphs.size(), 0);

  // Check host graph: verify we have key placeholder and sequencer.
  std::unique_ptr<FunctionBody> host_fbody;
  AttrValue device_ordinal_temp_value;
  device_ordinal_temp_value.set_i(0);
  protobuf::Map<string, AttrValue> host_func_attrs;
  host_func_attrs["_device_ordinal"] = device_ordinal_temp_value;
  TF_CHECK_OK(FunctionDefToBodyHelper(

      LIST_CASE(tensor, TF_ATTR_FUNC);
#undef LIST_CASE
      break;

    case tensorflow::AttrValue::kPlaceholder:
      *list_size = -1;
      *total_size = -1;
      break;

    case tensorflow::AttrValue::kFunc:
      *list_size = -1;
      *total_size = -1;
      break;

    case tensorflow::AttrValue::VALUE_NOT_SET:
      status->status = absl::InvalidArgumentError(

                   TensorShape expected_shape, error::Code expected_code) {
  Status status;
  NodeDef def;
  def.set_op("Bitcast");
  def.set_device(DEVICE_CPU);

  AttrValue typeAttr;
  SetAttrValue(input_tensor->dtype(), &typeAttr);

  AttrValue outTypeAttr;
  SetAttrValue(out_type, &outTypeAttr);

  (*def.mutable_attr())["T"] = typeAttr;
  (*def.mutable_attr())["type"] = outTypeAttr;

  def.add_input(

                boolean attrValue = reader.nextBoolean();
                attributes = attributesFactory.concat(attributes, Attribute.of(attrName, Boolean.class), attrValue);
            } else if (reader.peek() == NUMBER) {
                Integer attrValue = reader.nextInt();
                attributes = attributesFactory.concat(attributes, Attribute.of(attrName, Integer.class), attrValue);
            } else {

                final String attrValue
                @Inject ChangingAttributeRule(String attrValue) { this.attrValue = attrValue }
                void execute(ComponentMetadataContext context) {
                    context.details.allVariants {
                        attributes.attribute(Attribute.of("my.attribute.name", String), attrValue)
                    }
                }
            }

                                                     .begin())>::value>::type>
Status SetTypeAttribute(absl::string_view name, ContainerT types,
                        AttrValueMap* values) {
  AttrValue value;
  auto& type_list = *value.mutable_list();
  for (auto type : types) {
    DataType dtype;
    TF_RETURN_IF_ERROR(ConvertScalarTypeToDataType(type, &dtype));
    type_list.add_type(dtype);
  }

  }
  return ret;
}

string PrintTensor(const TensorProto& tensor_proto);

string PrintTensorProto(const TensorProto& proto);

string PrintAttrValue(StringPiece, const AttrValue& attr_value);

bool IsEmptyList(const AttrValue::ListValue& list);

string ToCamelCase(StringPiece str);

string SeparateNamespaces(StringPiece str);

// Returns a <string, bool> pair. The string is the C++ type name to be used for

        else strides
    )
    strides_attr = attr_value_pb2.AttrValue(
        list=attr_value_pb2.AttrValue.ListValue(i=strides_to_check)
    )

    if enable_per_channel_quantization:
      quantized_axis_attr = attr_value_pb2.AttrValue(i=3)
      quantized_dim_size_attr = attr_value_pb2.AttrValue(
          list=attr_value_pb2.AttrValue.ListValue(
              shape=[