result = session->Run(run_options_proto, input_pairs, output_tensor_names,
                          target_oper_names, &outputs, &run_metadata_proto);

    // Serialize back to upstream client, who now owns the new buffer
    if (run_metadata != nullptr) {
      status->status = MessageToBuffer(run_metadata_proto, run_metadata);
      if (!status->status.ok()) return;
    }
  } else {

  // Now load a block of a different file "b" at timestamp `now` + 1
  env->SetNowSeconds(now + 1);
  TF_EXPECT_OK(ReadCache(&cache, "b", 0, 1, &out));
  // Now load a different block of file "a" at timestamp `now` + 1. When the
  // first block of "a" expires, this block should also be removed because it
  // also belongs to file "a".
  TF_EXPECT_OK(ReadCache(&cache, "a", 8, 1, &out));

  while (!stop_pruning_thread_.WaitForNotificationWithTimeout(
      absl::Microseconds(1000000))) {
    absl::MutexLock lock(&mu_);
    uint64_t now = timer_seconds_();
    while (!lra_list_.empty()) {
      auto it = block_map_.find(lra_list_.back());
      if (now - it->second->timestamp <= max_staleness_) {
        // The oldest block is not yet expired. Come back later.
        break;
      }

  TF_DeleteAbstractOp(matmul_op);

  TF_AbstractFunction* func =
      TF_FinalizeFunction(graph_ctx, mm_outputs, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  /* Now that the graph is built, test graph implementation on matmul example:
    [[1,1] ,   *   [[1,1] ,   =  [[2,2],
     [1,1]]         [1,1]]        [2,2]]
  */

  // Build eager context.

#include <memory>

#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_internal.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/cloud/now_seconds_env.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace {

TEST(ExpiringLRUCacheTest, MaxAge) {
  const string key = "a";
  std::unique_ptr<NowSecondsEnv> env(new NowSecondsEnv);

      context.get(), components, device_name, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  // Run a collective sum, so each component should now be the same.
  TensorHandlePtr reduced(
      CollectiveSum(context.get(), parallel_value.get(), 2, status.get()));
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

        << dirpath;

    TF_DeleteFile(filepath.c_str(), s);
    ASSERT_TF_OK(s) << "TF_DeleteFile failed for " << filepath << ": "
                    << TF_Message(s);

    // Now deleting the directory should work.
    TF_DeleteDir(dirpath.c_str(), s);
    ASSERT_TF_OK(s) << "TF_DeleteDir failed for " << dirpath << ": "
                    << TF_Message(s);

    TF_DeleteStatus(s);
    break;

        fs->TranslateName(tensorflow::strings::StrCat(GetParam(), ":///")),
        "/");
    EXPECT_EQ(
        fs->TranslateName(tensorflow::strings::StrCat(GetParam(), ":////")),
        "/");
  }

  // Now test several paths/URIs
  EXPECT_EQ(GetRelativePath(fs->TranslateName(GetURIForPath("a_file"))),
            "/a_file");
  EXPECT_EQ(GetRelativePath(fs->TranslateName(GetURIForPath("a_dir/a_file"))),

  EXPECT_EQ(attr_value.type(), tensorflow::DT_INT32);
  ASSERT_TRUE(GetAttrValue(add, "N", &attr_value, s)) << TF_Message(s);
  EXPECT_EQ(attr_value.i(), 2);

  // Placeholder oper now has a consumer.
  ASSERT_EQ(1, TF_OperationOutputNumConsumers(TF_Output{feed, 0}));
  TF_Input feed_port;
  EXPECT_EQ(1, TF_OperationOutputConsumers(TF_Output{feed, 0}, &feed_port, 1));
  EXPECT_EQ(add, feed_port.oper);

  void Run(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,
           TF_Operation* output, int32_t expected_result) {
    Run(inputs, {{output, 0}}, {expected_result});
  }

  // Run the host graph, which now contains a function and check that
  // outputs are as expected.
  // 'T' stands for 'tensor' since the outputs are tensors, not scalars.
  void RunT(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,