}

Status ModularRandomAccessFile::Read(uint64 offset, size_t n,
                                     StringPiece* result, char* scratch) const {
  if (ops_->read == nullptr)
    return errors::Unimplemented(
        tensorflow::strings::StrCat("Read() not implemented for ", filename_));

  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);
  int64_t read =

                          char* buffer, TF_Status* status) -> int64_t {
    calls++;
    memset(buffer, 'x', n);
    TF_SetStatus(status, TF_OK, "");
    return n;
  };
  std::vector<char> out;
  // Our fake environment is initialized with the current timestamp.
  std::unique_ptr<NowSecondsEnv> env(new NowSecondsEnv);
  uint64 now = Env::Default()->NowSeconds();
  env->SetNowSeconds(now);

  return cache_size_;
}

void RamFileBlockCache::Prune() {
  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_) {

  EXPECT_EQ(
      1, metrics->point_set_map.at("test/counter")->points.at(0)->int64_value);

  TFE_MonitoringCounterCellIncrementBy(cell, 5);
  EXPECT_EQ(TFE_MonitoringCounterCellValue(cell), 6);
  metrics = collection_registry->CollectMetrics(options);
  EXPECT_EQ(
      6, metrics->point_set_map.at("test/counter")->points.at(0)->int64_value);

  TFE_MonitoringDeleteCounter0(counter);

  /* Want to test simple MatMul example:
    [[0,0],    *   [[0,0],    =   [[0,0],
     [0,0]]         [0,0]]         [0,0]]
  */

  // Build an abstract input tensor.
  int64_t dims[] = {2, 2};  // Matrices will be 2 x 2
  int num_dims = sizeof(dims) / sizeof(dims[0]);

  float vals[] = {0.0f, 0.0f, 0.0f, 0.0f};
  TFE_Context* eager_ctx = TF_ExecutionContextGetTFEContext(ctx, status.get());

}

// Checks whether the stride array matches the layout of compact, row-majored
// data.
bool IsValidStrideCompactRowMajorData(int64_t* shape_arr, int64_t* stride_arr,
                                      int ndim) {
  bool valid = true;
  int64_t expected_stride = 1;
  for (int i = ndim - 1; i >= 0; --i) {
    // Empty tensors are always compact regardless of strides.
    if (shape_arr[i] == 0) return true;

                                                  TF_Status* status);
TF_CAPI_EXPORT extern int64_t TFE_TensorHandleNumElements(TFE_TensorHandle* h,
                                                          TF_Status* status);
// This function will block till the operation that produces `h` has completed.
TF_CAPI_EXPORT extern int64_t TFE_TensorHandleDim(TFE_TensorHandle* h,
                                                  int dim_index,

// reinitialized (e.g. cleared caches) as well.
TF_CAPI_EXPORT extern uint64_t TFE_GetContextId(TFE_Context* ctx);

// -----------------------------------------------------------------------------
// Cancellation APIs.

typedef struct TFE_CancellationManager TFE_CancellationManager;
typedef int64_t TFE_CancellationToken;
typedef struct TFE_CancelCallback {
  void (*callback)(void* context);

  TapeVSpace vspace(ctx);
  std::vector<int64_t> target_tensor_ids = MakeTensorIDList(targets);
  std::vector<int64_t> source_tensor_ids = MakeTensorIDList(sources);
  tensorflow::gtl::FlatSet<int64_t> sources_set(source_tensor_ids.begin(),
                                                source_tensor_ids.end());
  std::unordered_map<int64_t, TapeTensor> sources_that_are_targets;

namespace tf_random_access_file {
using ReadFn =
    std::function<int64_t(const std::string& path, uint64_t offset, size_t n,
                          char* buffer, TF_Status* status)>;
typedef struct GCSFile {
  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);