tf_gcs_filesystem::ExpiringLRUCache<int> cache1(0, 4);
  cache1.Insert("a", 1);
  int value = 0;
  EXPECT_FALSE(cache1.Lookup("a", &value));
  // Now set max_age = 1 and verify the LRU eviction logic.
  tf_gcs_filesystem::ExpiringLRUCache<int> cache2(1, 4);
  cache2.Insert("a", 1);
  cache2.Insert("b", 2);
  cache2.Insert("c", 3);
  cache2.Insert("d", 4);
  EXPECT_TRUE(cache2.Lookup("a", &value));
  EXPECT_EQ(value, 1);

        << entry.InitializationErrorString();
    for (int i = 0; i < entry.slices_size(); ++i) {
      const auto& slice_proto = entry.slices(i);
      CHECK(filtered_keys
                .insert(EncodeTensorNameSlice(
                    string(v2_reader_->key()) /* full var's name */,
                    TensorSlice(slice_proto)))
                .second);
    }
  }

      ABSL_EXCLUSIVE_LOCKS_REQUIRED(mu_) {
    lru_list_.push_front(key);
    Entry entry{timer_seconds_(), value, lru_list_.begin()};
    auto insert = cache_.insert(std::make_pair(key, entry));
    if (!insert.second) {
      lru_list_.erase(insert.first->second.lru_iterator);
      insert.first->second = entry;
    } else if (max_entries_ > 0 && cache_.size() > max_entries_) {
      cache_.erase(lru_list_.back());

  std::unique_ptr<tensorflow::GrpcServer> worker_server1;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server1)
                  .ok());
  ASSERT_TRUE(worker_server1->Start().ok());

  server_def.set_task_index(2);
  std::unique_ptr<tensorflow::GrpcServer> worker_server2;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(

      "' (available: ");
  // Ensure deterministic (sorted) order in the error message
  std::set<string> factories_sorted;
  for (const auto& factory : GetFactories())
    factories_sorted.insert(factory.first);
  const char* comma = "";
  for (const string& factory : factories_sorted) {
    msg += comma + factory;
    comma = ", ";
  }
  msg += ")";

  return errors::InvalidArgument(msg.c_str());
}

        if (model_outputs[i]) {
          output_list.outputs.emplace_back(model_outputs[i]);
          output_list.expected_num_outputs += 1;
        } else {
          null_indices.insert(i);
        }
      }
      TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingContext>(func_ctx.get())
                             ->Finalize(&output_list, &func));
      scoped_func.reset(func);

    if len(testsuite) == 0:  # pylint: disable=g-explicit-length-test
      r._elem.remove(testsuite._elem)
  if len(r) > 0:  # pylint: disable=g-explicit-length-test
    result += r

# Insert the number of failures for each test to help identify flakes
# need to clarify for shard
for p in result._elem.xpath(".//error | .//failure"):
  key = re.sub(r"0x\w+", "", p.getparent().get("name", "")) + p.text

    ASSERT_EQ(errors::OK, status_.code()) << status_.message();
    Y.reset(Y_raw);
  }

  std::vector<AbstractTensorHandle*> outputs(1);
  status_ = RunModel(ReluGradModel, immediate_execution_ctx_.get(), {Y.get()},
                     absl::MakeSpan(outputs), UseFunction());
  ASSERT_EQ(errors::OK, status_.code()) << status_.message();

    ASSERT_TF_OK(s) << "TF_GetChildren failed for " << dirpath;
    const char* childpath;
    ASSERT_TRUE(TF_StringStreamNext(children, &childpath));
    ASSERT_EQ(::tensorflow::string(childpath), "somefile.txt");
    // There should only be one file in this directory.
    ASSERT_FALSE(TF_StringStreamNext(children, &childpath));
    ASSERT_EQ(childpath, nullptr);
    TF_StringStreamDone(children);

    status_ = StatusFromTF_Status(status.get());
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();

    {
      AbstractContext* ctx_raw = nullptr;
      status_ =
          BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);
      ASSERT_EQ(errors::OK, status_.code()) << status_.message();
      immediate_execution_ctx_.reset(ctx_raw);
    }