desc->node_builder.Attr(
      attr_name, ArraySlice<const DataType>(
                     reinterpret_cast<const DataType*>(values), num_values));
}

void TF_SetAttrPlaceholder(TF_OperationDescription* desc, const char* attr_name,
                           const char* placeholder) {
  tensorflow::AttrValue attr_value;
  attr_value.set_placeholder(placeholder);
  desc->node_builder.Attr(attr_name, attr_value);
}

  }
  tsl::Set_TF_Status_from_Status(s, tracing_op->SetOpName(op_name));
}

void TF_AbstractOpSetAttrType(TF_AbstractOp* op, const char* const attr_name,
                              TF_DataType value, TF_Status* s) {
  Status status =
      unwrap(op)->SetAttrType(attr_name, static_cast<DataType>(value));
  TF_SetStatus(s, static_cast<TF_Code>(status.code()),
               tsl::NullTerminatedMessage(status));
}

void TF_AttrBuilderSetType(TF_AttrBuilder* builder, const char* attr_name,
                           TF_DataType value) {
  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, static_cast<tensorflow::DataType>(value));
}

void TF_AttrBuilderSetTypeList(TF_AttrBuilder* builder, const char* attr_name,
                               const TF_DataType* values, int num_values) {

  return op_->SetAttrInt(attr_name, value);
}
Status SetAttrFloat(AbstractOperation* op_, const char* attr_name, float value,
                    ForwardOperation* forward_op_) {
  forward_op_->attrs.Set(attr_name, value);
  return op_->SetAttrFloat(attr_name, value);
}
Status SetAttrBool(AbstractOperation* op_, const char* attr_name, bool value,

    return;
  }

  (*(fdef_or.value()->mutable_attr()))[string(attr_name)] = attr_value;

  status->status = absl::OkStatus();
}

void TF_FunctionGetAttrValueProto(TF_Function* func, const char* attr_name,
                                  TF_Buffer* output_attr_value,
                                  TF_Status* status) {
  const auto& it = func->record->fdef().attr().find(attr_name);
  if (it == func->record->fdef().attr().end()) {

      "      attr {"
      "        key: 'dtype'"
      "        value {"
      "          type: DT_FLOAT"
      "        }"
      "      }"
      "    }"
      "    node_def {"
      "      name: 'read1'"
      "      op: 'ReadVariableOp'"
      "      input: 'var'"
      "      device: '/job:localhost/replica:0/task:1/device:CPU:0'"
      "      attr {"
      "        key: 'dtype'"

  Status SetAttrFloat(const char* attr_name, float value) override {
    op_->node_builder.Attr(attr_name, value);
    return absl::OkStatus();
  }
  Status SetAttrBool(const char* attr_name, bool value) override {
    op_->node_builder.Attr(attr_name, value);
    return absl::OkStatus();
  }
  Status SetAttrType(const char* const attr_name, DataType value) override {
    if (!op_) {
      return Status(

  }
}

void TFE_OpSetAttrInt(TFE_Op* op, const char* attr_name, int64_t value) {
  auto s = tensorflow::unwrap(op)->SetAttrInt(attr_name, value);
  if (!s.ok()) {
    LOG(WARNING) << "Unable to set attribute: " << attr_name;
  }
}

void TFE_OpSetAttrFloat(TFE_Op* op, const char* attr_name, float value) {
  auto s = tensorflow::unwrap(op)->SetAttrFloat(attr_name, value);

    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  }

  void GetAttr(const char* attr_name, AttrValue* out_attr) {
    TF_Buffer* attr_buf = TF_NewBuffer();
    TF_FunctionGetAttrValueProto(func_, attr_name, attr_buf, s_);
    ASSERT_TRUE(out_attr->ParseFromArray(attr_buf->data, attr_buf->length));
    TF_DeleteBuffer(attr_buf);
  }

  const char* func_name_ = "MyFunc";
  const char* func_node_name_ = "MyFunc_0";

  tensorflow::AttrValueMap attr_values = ExtractAttrs(minOp);
  tensorflow::AttrValueMap::const_iterator attr_found = attr_values.find("T");
  EXPECT_NE(attr_found, attr_values.cend());
  EXPECT_EQ(attr_found->second.type(), tensorflow::DataType::DT_FLOAT);
  attr_found = attr_values.find("Tidx");
  EXPECT_NE(attr_found, attr_values.cend());
  EXPECT_EQ(attr_found->second.type(), tensorflow::DataType::DT_INT32);