});

  uint64_t stat_cache_max_age = kStatCacheDefaultMaxAge;
  size_t stat_cache_max_entries = kStatCacheDefaultMaxEntries;
  const char* stat_cache_max_age_env = std::getenv(kStatCacheMaxAge);
  if (stat_cache_max_age_env &&
      absl::SimpleAtoi(stat_cache_max_age_env, &value)) {
    stat_cache_max_age = value;
  }
  const char* stat_cache_max_entries_env = std::getenv(kStatCacheMaxEntries);

                  .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(
                  server_def, tensorflow::Env::Default(), &worker_server2)
                  .ok());
  ASSERT_TRUE(worker_server2->Start().ok());

  TF_Status* status = TF_NewStatus();

        // broadcast, i.e. set as the input to each parallel operation. This
        // allows code like "tf.constant(1.)" or "tf.reduce_sum(..., axis=1)"
        // (where the value starts on the host), without allowing other implicit
        // copies/broadcasts. Other implicit copies may be supported eventually,
        // but need special handling for gradients (gradient of copy-on is not

  }
  // Calculate the block-aligned start and end of the read.
  size_t start = block_size_ * (offset / block_size_);
  size_t finish = block_size_ * ((offset + n) / block_size_);
  if (finish < offset + n) {
    finish += block_size_;
  }
  size_t total_bytes_transferred = 0;
  // Now iterate through the blocks, reading them one at a time.
  for (size_t pos = start; pos < finish; pos += block_size_) {

  ASSERT_TRUE(
      factory->NewServer(server_def, ServerFactory::Options(), &w0).ok());
  ASSERT_TRUE(w0->Start().ok());
  server_def.set_task_index(1);
  std::unique_ptr<tensorflow::ServerInterface> w1;
  ASSERT_TRUE(
      factory->NewServer(server_def, ServerFactory::Options(), &w1).ok());
  ASSERT_TRUE(w1->Start().ok());

  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();

  // one second of wall time), it should remove "a" and leave "b" alone.
  uint64 start = Env::Default()->NowSeconds();
  do {
    Env::Default()->SleepForMicroseconds(100000);
  } while (cache.CacheSize() == 24 && Env::Default()->NowSeconds() - start < 3);
  // There should be one block left in the cache, and it should be the first
  // block of "b".
  EXPECT_EQ(cache.CacheSize(), 8);

                  .ok());
  ASSERT_TRUE(worker_server1->Start().ok());
  server_def_0.set_task_index(2);
  std::unique_ptr<tensorflow::GrpcServer> worker_server2;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def_0, tensorflow::Env::Default(), &worker_server2)
                  .ok());
  ASSERT_TRUE(worker_server2->Start().ok());

  TFE_Context* ctx_0 = CreateContext(serialized_server_def_0,

  TF_DeleteExecutionContext(ctx);
}

TEST_P(UnifiedCAPI, TestBasicGraph) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);

  // Start a new function / execution context.
  string fn_name = "double";
  TF_ExecutionContext* graph_ctx =
      TF_CreateFunction(fn_name.c_str(), status.get());

              {"add2:sum:0", "final_out"}},
             {});
}

TEST_F(CApiFunctionTest, ImportFunctionDef_InvalidProto) {
  // Invalid protobuf data (protos cannot start with 4 bytes of zeros)
  char proto[] = {0x0, 0x0, 0x0, 0x0};
  func_ = TF_FunctionImportFunctionDef(proto, 4, s_);
  EXPECT_TRUE(func_ == nullptr);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_));