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,

                        const char* attr_name, const char* attr_value,
                        TF_Operation** op) {
  TF_OperationDescription* desc = TF_NewOperation(graph, "Placeholder", name);
  TF_SetAttrType(desc, "dtype", TF_INT32);
  TF_SetAttrString(desc, attr_name, attr_value, strlen(attr_value));
  *op = TF_FinishOperation(desc, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  return ret;
}

bool GetAttrValue(TF_Operation* oper, const char* attr_name,
                  tensorflow::AttrValue* attr_value, TF_Status* s) {
  TF_Buffer* buffer = TF_NewBuffer();
  TF_OperationGetAttrValueProto(oper, attr_name, buffer, s);
  bool ret = TF_GetCode(s) == TF_OK;
  if (ret) ret = attr_value->ParseFromArray(buffer->data, buffer->length);
  TF_DeleteBuffer(buffer);
  return ret;
}

                                                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,

  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";

  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.
  EXPECT_EQ(-1, TF_OperationOutputListLength(feed, "bogus", s));

#ifndef TENSORFLOW_C_EAGER_ABSTRACT_OP_ATTRS_H_
#define TENSORFLOW_C_EAGER_ABSTRACT_OP_ATTRS_H_

#include "absl/container/inlined_vector.h"
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Attributes of an op.
class AbstractOpAttrs {
 protected:

*   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

            attributes: list[HTMLLinkAttribute] = []
            for attr_name, attr_quote, attr_value in re.findall(
                HTML_ATTR_RE, link_open_tag
            ):
                assert isinstance(attr_name, str)
                assert isinstance(attr_quote, str)
                assert isinstance(attr_value, str)
                attributes.append(
                    HTMLLinkAttribute(