// Set attr
  tensorflow::AttrValue attr;
  attr.set_s("test_attr_value");
  string bytes;
  attr.SerializeToString(&bytes);
  TF_FunctionSetAttrValueProto(func_, "test_attr_name", bytes.data(),
                               bytes.size(), s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

  // Get attr
  AttrValue read_attr;
  GetAttr("test_attr_name", &read_attr);

        break;
      }
      case ::tensorflow::AttrValue::kI:
        flex_builder->Int(key, attr.i());
        break;
      case ::tensorflow::AttrValue::kF:
        flex_builder->Float(key, attr.f());
        break;
      case ::tensorflow::AttrValue::kB:
        flex_builder->Bool(key, attr.b());
        break;
      case tensorflow::AttrValue::kList:
        if (attr.list().s_size() > 0) {

static Status ValidateNode(const NodeDef& node) {
  const auto node_iterator = node.attr().find("value");
  if (node_iterator != node.attr().end()) {
    AttrValue node_value = node_iterator->second;
    if (node_value.has_tensor()) {
      const PartialTensorShape node_shape(node_value.tensor().tensor_shape());
      if (node_shape.num_elements() < 0) {

                                                TF_Status* status);

// `proto` should point to a sequence of bytes of length `proto_len`
// representing a binary serialization of an AttrValue protocol
// buffer.
TF_CAPI_EXPORT extern void TF_SetAttrValueProto(TF_OperationDescription* desc,
                                                const char* attr_name,

    *uncompilable_reason =
        "SymbolicGradient should be handled by IsCompilableCall().";
    return false;
  }

  if (node.type_string() == "Const") {
    const AttrValue* attr = node.attrs().Find("dtype");
    if (!op_filter_.allow_string_consts && attr != nullptr &&
        attr->type() == DT_STRING) {
      *uncompilable_reason =

  TFE_DeleteTensorHandle(tensor_handle);

  tensorflow::AttrValue i_list_values;
  for (int i = 0; i < 4; ++i) {
    i_list_values.mutable_list()->add_i(1);
  }
  SetOpAttrValueScalar(ctx, op, i_list_values, "ksize", status);
  SetOpAttrValueScalar(ctx, op, i_list_values, "strides", status);

  tensorflow::AttrValue padding_value;
  *padding_value.mutable_s() = "VALID";

                                                TF_Buffer* buf,
                                                TF_Status* status);

// Set an op's attribute from a serialized AttrValue protocol buffer.
//
// Analogous to TF_SetAttrValueProto for building graph operations.
TF_CAPI_EXPORT extern void TFE_OpSetAttrValueProto(const TFE_Op* op,

  EXPECT_EQ(0, TF_OperationOutputNumConsumers(TF_Output{feed, 0}));
  EXPECT_EQ(0, TF_OperationNumControlInputs(feed));
  EXPECT_EQ(0, TF_OperationNumControlOutputs(feed));

  tensorflow::AttrValue attr_value;
  ASSERT_TRUE(GetAttrValue(feed, "dtype", &attr_value, s)) << TF_Message(s);
  EXPECT_EQ(attr_value.type(), tensorflow::DT_INT32);

  // Test not found errors in TF_Operation*() query functions.

      continue;
    }

    if (node->type_string() == "Const") {
      // Skip Const op with type DT_STRING, since XLA autoclustering doesn't
      // support it.
      const AttrValue* attr = node->attrs().Find("dtype");
      if (attr != nullptr && attr->type() == DT_STRING) {
        continue;
      }
    }

    if (!allowlist.empty() && !allowlist.contains(node->def().op())) {

*   New `Variable`s are not added to the same collection several times even if a
    list with duplicates is passed to the constructor.
*   The Python API will now properly set the `list` member of `AttrValue` in
    constructed `GraphDef` messages for empty lists. The serialization of some
    graphs will change, but the change is both forwards and backwards