absl::MutexLock l(&block->mu);
  if (block->state != FetchState::FINISHED) {
    return true;  // No need to check for staleness.
  }
  if (max_staleness_ == 0) return true;  // Not enforcing staleness.
  return timer_seconds_() - block->timestamp <= max_staleness_;
}

std::shared_ptr<RamFileBlockCache::Block> RamFileBlockCache::Lookup(
    const Key& key) {
  absl::MutexLock lock(&mu_);

  env->SetNowSeconds(6);
  EXPECT_FALSE(cache.Lookup(key, &value));
}

TEST(ExpiringLRUCacheTest, MaxEntries) {
  // max_age of 0 means nothing will be cached.
  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);

    EXPECT_EQ(got_n, want_n);
    calls++;
    memset(buffer, 'x', got_n);
    TF_SetStatus(status, TF_OK, "");
    return got_n;
  };
  // If block_size, max_bytes, or both are zero, or want_n is larger than
  // max_bytes the cache is a pass-through.
  tf_gcs_filesystem::RamFileBlockCache cache1(1, 0, 0, fetcher);
  tf_gcs_filesystem::RamFileBlockCache cache2(0, 1, 0, fetcher);

  return {ToOperation(edge->src()), edge->src_output()};
}

void TF_OperationAllInputs(TF_Operation* oper, TF_Output* inputs,
                           int max_inputs) {
  for (auto* edge : oper->node.in_edges()) {
    if (edge->dst_input() >= 0 && edge->dst_input() < max_inputs) {
      inputs[edge->dst_input()] = {ToOperation(edge->src()),
                                   edge->src_output()};
    }
  }
}

          uint64_t block_size, size_t max_bytes, uint64_t max_staleness,
          uint64_t stat_cache_max_age, size_t stat_cache_max_entries);
} GCSFile;

// This function is used to initialize a filesystem without the need of setting
// manually environement variables.
void InitTest(TF_Filesystem* filesystem, bool compose, uint64_t block_size,
              size_t max_bytes, uint64_t max_staleness,

                        const TFE_OpAttrs* attributes, int expected_max_outputs,
                        TF_Status* status) const {
  std::vector<PartialTensorShape> expected_output_shapes(expected_max_outputs);
  StartExecute(context, inputs, operation_name, attributes,
               expected_max_outputs, *default_cancellation_manager_);
  auto result = Join(expected_output_shapes, status);

// an array of length at least `max_inputs` (ideally set to
// TF_OperationNumInputs(oper)).  Beware that a concurrent
// modification of the graph can increase the number of inputs of
// an operation.
TF_CAPI_EXPORT extern void TF_OperationAllInputs(TF_Operation* oper,
                                                 TF_Output* inputs,
                                                 int max_inputs);

    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_Execute(matmul, &retvals[0], &num_retvals, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    if (state.iterations() >= state.max_iterations && async) {
      TFE_Executor* executor = TFE_ContextGetExecutorForThread(ctx);
      TFE_ExecutorWaitForAllPendingNodes(executor, status);
      ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

action_spec {
  action: CHECK_FILE_COUNT
  file_count_params {
    rule_selector {
      rule_kind_regex: "^.*py(type)?(_strict)?_(binary|library|test).*$"
      generator_function_regex: "^(?!boq_header)$"
    }
    max_source_count: 1
  }

  absl::optional<std::vector<std::unique_ptr<ParallelTensor>>> Execute(
      TFE_Context* context, const std::vector<ParallelTensor*>& inputs,
      const char* operation_name, const TFE_OpAttrs* attributes,
      int expected_max_outputs, TF_Status* status) const;

  // A non-blocking version of `Execute`. After each call, `Join` must be called
  // before `StartExecute` is called again. Using `StartExecute` with `Join`