return nullptr;
#else
  TF_Library* lib_handle = new TF_Library;
  static tensorflow::mutex mu(tensorflow::LINKER_INITIALIZED);
  static std::unordered_map<std::string, void*>* loaded_libs =
      new std::unordered_map<std::string, void*>();
  tensorflow::Env* env = tensorflow::Env::Default();
  {
    tensorflow::mutex_lock lock(mu);
    auto it = loaded_libs->find(library_filename);
    if (it != loaded_libs->end()) {

  ASSERT_GE(TF_NowNanos(), 946684800 * 1e9);
}

namespace {

struct SomeThreadData {
  ::tensorflow::mutex mu;
  bool did_work = false;
};

void SomeThreadFunc(void* data) {
  auto* real_data = static_cast<SomeThreadData*>(data);
  ::tensorflow::mutex_lock l(real_data->mu);
  real_data->did_work = true;
}

}  // namespace

TEST(TestEnv, TestThreads) {

#include <functional>
#include <list>
#include <map>
#include <memory>
#include <string>

#include "absl/base/thread_annotations.h"
#include "absl/synchronization/mutex.h"
#include "tensorflow/c/env.h"
#include "tensorflow/c/tf_status.h"

namespace tf_gcs_filesystem {

/// \brief An LRU cache of string keys and arbitrary values, with configurable

    std::list<Key>::iterator lra_iterator;
    /// The timestamp (seconds since epoch) at which the block was cached.
    uint64_t timestamp;
    /// Mutex to guard state variable
    absl::Mutex mu;
    /// The state of the block.
    FetchState state ABSL_GUARDED_BY(mu) = FetchState::CREATED;
    /// Wait on cond_var if state is FETCHING.
    absl::CondVar cond_var;
  };

  const std::string path;
  const bool is_cache_enable;
  const uint64_t buffer_size;
  ReadFn read_fn;
  absl::Mutex buffer_mutex;
  uint64_t buffer_start ABSL_GUARDED_BY(buffer_mutex);
  bool buffer_end_is_past_eof ABSL_GUARDED_BY(buffer_mutex);
  std::string buffer ABSL_GUARDED_BY(buffer_mutex);

  GCSFile(std::string path, bool is_cache_enable, uint64_t buffer_size,
          ReadFn read_fn)
      : path(path),

        "//tensorflow/core:framework",
        "//tensorflow/core:framework_internal_impl",
        "//tensorflow/core/lib/gtl:cleanup",
        "//tensorflow/core/platform:errors",
        "//tensorflow/core/platform:mutex",
        "//tensorflow/core/platform:refcount",
    ] + if_not_mobile([
        "//tensorflow/core/kernels:tensor_list",
        "//tensorflow/core/kernels:tensor_list_util",

    TF_RETURN_IF_ERROR(tensorflow::TensorShapeUtils::MakeShape(dims, shape));

    return absl::OkStatus();
  }

  tensorflow::FullTypeDef FullType() const override {
    const FullTypeDef* ft;
    mutex_lock l(graph_->mu);
    graph_->graph.NodeType(output_.oper->node.name(), &ft);
    if (ft == nullptr) {
      return FullTypeDef();
    } else {
      return *ft;
    }
  }

  TF_Output output_;

#include "tensorflow/core/lib/monitoring/gauge.h"
#include "tensorflow/core/lib/monitoring/sampler.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/mutex.h"
#include "tensorflow/core/platform/strcat.h"
#include "tsl/framework/cancellation.h"

using tensorflow::string;

void TFE_OpReset(TFE_Op* op_to_reset, const char* op_or_function_name,

  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

  TF_DeleteFunction(grad_func);

  const StackTracesMap* func_stack_traces;
  const StackTracesMap* grad_stack_traces;

  {
    mutex_lock l(host_graph_->mu);
    auto flib_def = host_graph_->graph.flib_def();
    func_stack_traces = flib_def.GetStackTraces(func_name_);
    grad_stack_traces = flib_def.GetStackTraces("MyGrad");
  }

#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/platform/mutex.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/public/session.h"

namespace tensorflow {
class Device;
class DeviceMgr;
class ServerInterface;