const float* values, int num_values) {
  auto s =
      tensorflow::unwrap(op)->SetAttrFloatList(attr_name, values, num_values);
  if (!s.ok()) {
    LOG(WARNING) << "Unable to set attribute: " << attr_name;
  }
}

void TFE_OpSetAttrIntList(TFE_Op* op, const char* attr_name,
                          const int64_t* values, int num_values) {
  auto s =

  } else {
    // Block until the key-value is set or the worker shuts down.
    timeout = absl::InfiniteDuration();
  }
  auto status_or_value = coord_agent->GetKeyValue(key, timeout);
  status->status = status_or_value.status();
  if (!status_or_value.ok()) return;

  const std::string& value_string = status_or_value.value();
  void* data = tensorflow::port::Malloc(value_string.length());

  memcpy(TF_TensorData(t.get()), &value, TF_TensorByteSize(t.get()));

  std::unique_ptr<TFE_TensorHandle, decltype(&TFE_DeleteTensorHandle)>
      value_handle(TFE_NewTensorHandle(t.get(), status),
                   TFE_DeleteTensorHandle);
  if (TF_GetCode(status) != TF_OK) return nullptr;

  TFE_OpAddInput(op, value_handle.get(), status);

                         int num_values) override {
    op_->node_builder.Attr(attr_name,
                           ArraySlice<const DataType>(values, num_values));
    return absl::OkStatus();
  }
  Status SetAttrBoolList(const char* attr_name, const unsigned char* values,
                         int num_values) override {
    std::unique_ptr<bool[]> b(new bool[num_values]);
    for (int i = 0; i < num_values; ++i) {

  const char* block_size_env = std::getenv(kBlockSize);
  if (block_size_env && absl::SimpleAtoi(block_size_env, &value)) {
    block_size = value * 1024 * 1024;
  }
  const char* max_bytes_env = std::getenv(kMaxCacheSize);
  if (max_bytes_env && absl::SimpleAtoi(max_bytes_env, &value)) {
    max_bytes = static_cast<size_t>(value * 1024 * 1024);
  }
  const char* max_staleness_env = std::getenv(kMaxStaleness);

namespace {

TEST(CAPI_EXPERIMENTAL, GetServerDefTest) {
  const string expected_text_proto(R"(cluster {
  job {
    name: "worker"
    tasks {
      key: 0
      value: "tpuserver:0"
    }
    tasks {
      key: 1
      value: "localhost:1"
    }
  }
}
job_name: "worker"
task_index: 1
protocol: "grpc"
)");

  TF_Status* status = TF_NewStatus();

                        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);

  std::vector<TF_Filesystem_Option_Value_Union> option_values(values.size());
  for (size_t i = 0; i < values.size(); i++) {
    memset(&option_values[i], 0, sizeof(option_values[i]));
    option_values[i].real_val = values[i];
  }
  option_value.values = &option_values[0];
  option.value = &option_value;
  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);

  EXPECT_TF_META("v", -1, TF_ATTR_TENSOR, -1);
  TF_Tensor* value;
  TF_OperationGetAttrTensor(oper, "v", &value, s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  ASSERT_NE(nullptr, value);
  EXPECT_EQ(TF_INT8, TF_TensorType(value));
  EXPECT_EQ(ndims, TF_NumDims(value));
  for (int i = 0; i < TF_NumDims(value); ++i) {
    EXPECT_EQ(dims[i], TF_Dim(value, i)) << i;
  }

  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";