"//tensorflow/c:logging",
        "//tensorflow/c:tf_status",
        "//tensorflow/c/experimental/filesystem:filesystem_interface",
        "@com_github_googlecloudplatform_google_cloud_cpp//:storage_client",
        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/types:variant",
    ],
)

cc_library(
    name = "gcs_helper",
    srcs = ["gcs_helper.cc"],
    hdrs = ["gcs_helper.h"],

// least `max_values`.
//
// The elements of values will point to addresses in `storage` which must be at
// least `storage_size` bytes in length.  Ideally, max_values would be set to
// TF_AttrMetadata.list_size and `storage` would be at least
// TF_AttrMetadata.total_size, obtained from TF_OperationGetAttrMetadata(oper,
// attr_name).
//
// Fails if storage_size is too small to hold the requested number of strings.

build:tf_public_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/january2024" --remote_upload_local_results=false
# Cache pushes are limited to TF's CI system.
build:tf_public_cache_push --config=tf_public_cache --remote_upload_local_results=true --google_default_credentials
# Public cache for macOS builds

apt-get update
apt-get -y build-dep patchelf/jammy
apt-get -b source patchelf/jammy

### Typical Pull Request Workflow -

**1. New PR**

- As a contributor, you submit a New PR on GitHub.
- We inspect every incoming PR and add certain labels to the PR such as `size:`,
  `comp:` etc.  At this stage we check if the PR is valid and meets certain
  quality requirements. For example, we check if the CLA is signed, PR has
  sufficient description, if applicable unit tests are added, if it is a

  /// Look up the entry with key `key` and copy it to `value` if found. If not
  /// found, call `compute_func`. If `compute_func` set `status` to `TF_OK`,
  /// store a copy of the output parameter in the cache, and another copy in
  /// `value`.
  void LookupOrCompute(const std::string& key, T* value,
                       const ComputeFunc& compute_func, TF_Status* status) {

                                                      TF_Status* status);

// Retrieves a list of directory names on the local machine that may be used for
// temporary storage. You can iterate through the list with TF_StringStreamNext.
// The caller is responsible for freeing the list (see TF_StringStreamDone).
TF_CAPI_EXPORT extern TF_StringStream* TF_GetLocalTempDirectories(void);

  void (*cleanup)(TF_WritableFile* file);

  /// Appends `buffer` of size `n` to `*file`.
  ///
  /// Core TensorFlow owns `buffer` and guarantees at least `n` bytes of storage
  /// that can be used to write data.
  ///
  /// Note: the `buffer` argument is NOT a null terminated string!
  ///
  /// Plugins:
  ///   * Must set `status` to `TF_OK` if exactly `n` bytes have been written.

        behavior, set `inject_prefetch=True` in
        `tf.data.experimental.OptimizationOptions`.
    *   Added a new value to `tf.data.Options.autotune.autotune_algorithm`:
        `STAGE_BASED`. If the autotune algorithm is set to `STAGE_BASED`, then
        it runs a new algorithm that can get the same performance with lower
        CPU/memory usage.
    *   Added

  ///
  /// The file block cache key is a {filename, offset} pair.
  typedef std::pair<std::string, size_t> Key;

  /// \brief The state of a block.
  ///
  /// A block begins in the CREATED stage. The first thread will attempt to read
  /// the block from the filesystem, transitioning the state of the block to
  /// FETCHING. After completing, if the read was successful the state should