block->data.resize(block_size_, 0);
        int64_t bytes_transferred;
        bytes_transferred = block_fetcher_(key.first, key.second, block_size_,
                                           block->data.data(), status);
        block->mu.Lock();  // Reacquire the lock immediately afterwards
        if (TF_GetCode(status) == TF_OK) {
          block->data.resize(bytes_transferred, 0);

  bool BlockNotStale(const std::shared_ptr<Block>& block)
      ABSL_EXCLUSIVE_LOCKS_REQUIRED(mu_);

  /// Look up a Key in the block cache.
  std::shared_ptr<Block> Lookup(const Key& key) ABSL_LOCKS_EXCLUDED(mu_);

  void MaybeFetch(const Key& key, const std::shared_ptr<Block>& block,
                  TF_Status* status) ABSL_LOCKS_EXCLUDED(mu_);

  /// Trim the block cache to make room for another entry.

    }
    TF_SetStatus(status, TF_OK, "");
    return bytes_transferred;
  };
  for (size_t block_size = 2; block_size <= 4; block_size++) {
    // Make a cache of N-byte block size (1 block) and verify that reads of
    // varying offsets and lengths return correct data.
    tf_gcs_filesystem::RamFileBlockCache cache(block_size, block_size, 0,
                                               fetcher);

// you want the latter, use TFE_TensorHandleBackingDeviceName. This function
// will block till the operation that produces `h` has completed.
TF_CAPI_EXPORT extern const char* TFE_TensorHandleDeviceName(
    TFE_TensorHandle* h, TF_Status* status);

// Returns the name of the device in whose memory `h` resides.
//
// This function will block till the operation that produces `h` has completed.

typedef struct GCSFile {
  google::cloud::storage::Client gcs_client;  // owned
  bool compose;
  absl::Mutex block_cache_lock;
  std::shared_ptr<RamFileBlockCache> file_block_cache
      ABSL_GUARDED_BY(block_cache_lock);
  uint64_t block_size;  // Reads smaller than block_size will trigger a read
                        // of block_size.
  std::unique_ptr<ExpiringLRUCache<GcsFileStat>> stat_cache;

    deps = [
        "//tensorflow/c:env",
    ],
)

cc_library(
    name = "cleanup",
    hdrs = ["cleanup.h"],
)

cc_library(
    name = "ram_file_block_cache",
    srcs = ["ram_file_block_cache.cc"],
    hdrs = ["ram_file_block_cache.h"],
    deps = [
        ":cleanup",
        "//tensorflow/c:env",
        "//tensorflow/c:logging",
        "//tensorflow/c:tf_status",

                    absl::optional<int64_t> step_id = absl::nullopt);
  // Block until the previous `StartExecute` operation has executed. Forwards
  // the status from `TFE_Execute` and returns outputs if the status is OK.
  std::vector<TensorHandlePtr> Join(TF_Status* status);

  // Block until all Ops finished running on the thread.
  void AsyncWait(TF_Status* status);

 private:
  void Run();

                 uint64_t block_size, size_t max_bytes, uint64_t max_staleness,
                 uint64_t stat_cache_max_age, size_t stat_cache_max_entries)
    : gcs_client(gcs_client),
      compose(compose),
      block_cache_lock(),
      block_size(block_size) {
  file_block_cache = std::make_unique<RamFileBlockCache>(
      block_size, max_bytes, max_staleness,

    extra_args = ["--allow-unsafe"],
    requirements_in = REQUIREMENTS_FILE_NAME,
    requirements_txt = "requirements_lock_3_9.txt",
)

compile_pip_requirements_3_10(
    name = "requirements_3_10",
    extra_args = ["--allow-unsafe"],
    requirements_in = REQUIREMENTS_FILE_NAME,
    requirements_txt = "requirements_lock_3_10.txt",
)

compile_pip_requirements_3_11(
    name = "requirements_3_11",

to the actual TensorFlow wheel.
3. Creates a `requirements_lock_<python_version>.txt` file.
4. Updates the `requirements_lock_<python_version>.txt` file using
a Bazel command.
5. Moves the updated `requirements_lock_<python_version>.txt` file
to the `../wheel_test/` directory.


### How it Works in the Presubmit Job

`_requirements_lock` files will be generated by the presubmit job. A detailed