# Usage: Go to
# https://github.com/tensorflow/tensorflow/actions/workflows/release-branch-cherrypick.yml
# and click "Run Workflow." Leave "Use Workflow From" set to "master", then
# input the branch name and paste the cherry-pick commit and click Run. A PR
# will be created.

name: Release Branch Cherrypick
on:
  workflow_dispatch:
    inputs:

# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")

tf_workspace3()

load("@//tensorflow:workspace2.bzl", "tf_workspace2")

tf_workspace2()

  const size_t max_entries_;

  /// The callback to read timestamps.
  std::function<uint64_t()> timer_seconds_;

  /// Guards access to the cache and the LRU list.
  absl::Mutex mu_;

  /// The cache (a map from string key to Entry).
  std::map<std::string, Entry> cache_ ABSL_GUARDED_BY(mu_);

  /// The LRU list of entries. The front of the list identifies the most
  /// recently accessed entry.

      pruning_thread_.reset();
    }
  }

  /// Read `n` bytes from `filename` starting at `offset` into `buffer`. It
  /// returns total bytes read ( -1 in case of errors ). This method will set
  /// `status` to:
  ///
  /// 1) The error from the remote filesystem, if the read from the remote
  ///    filesystem failed.
  /// 2) `TF_FAILED_PRECONDITION` if the read from the remote filesystem
  /// succeeded,

It is possible to run multiple TensorFlow models in parallel. For example,
`ModelServer` collates all computation graphs exposed to it (from multiple
`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the
inherent ones from multitenant configurations. The primary areas of concern are
tenant isolation, resource allocation, model sharing and hardware attacks.

// a map (`tensorflow::eager::TensorTape`) from the wrapped tensor to the id of
// the op that produced it (or -1 if this tensor was watched using
// `GradientTape::Watch`.) The op_id is simply a unique index assigned to each
// op executed under the tape. A separate map (`tensorflow::eager::OpTape`)
// maintains the map from `op_id` to a `OpTapeEntry` which stores the `op_type`,

                                                  TF_Status* status,
                                                  const char* description) {
  auto* result = new TFE_MonitoringCounter0({name, description});
  tsl::Set_TF_Status_from_Status(status, result->counter->GetStatus());
  if (!result->counter->GetStatus().ok()) {
    delete result;
    return nullptr;
  }
  return result;
}

    *   Upgrade Flatbuffers v2.0.5 from v1.12.0

*   `tf.keras`:

    *   `EinsumDense` layer is moved from experimental to core. Its import path
        is moved from `tf.keras.layers.experimental.EinsumDense` to
        `tf.keras.layers.EinsumDense`.
    *   Added `tf.keras.utils.audio_dataset_from_directory` utility to easily
        generate audio classification datasets from directories of `.wav` files.

      form, that is based on (or derived from) the Work and for which the
      editorial revisions, annotations, elaborations, or other modifications
      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,

# This is the official list of TensorFlow authors for copyright purposes.
# This file is distinct from the CONTRIBUTORS files.
# See the latter for an explanation.

# Names should be added to this file as:
# Name or Organization <email address>
# The email address is not required for organizations.

Google Inc.
Yuan Tang <******@****.***>