*   Added `tf.keras.utils.audio_dataset_from_directory` utility to easily
        generate audio classification datasets from directories of `.wav` files.
    *   Added `subset="both"` support in
        `tf.keras.utils.image_dataset_from_directory`,`tf.keras.utils.text_dataset_from_directory`,
        and `audio_dataset_from_directory`, to be used with the

                     TF_Status* status);

  /// Creates the directory specified by `path` and all needed ancestors.
  ///
  /// Plugins:
  ///   * Must set `status` to `TF_OK` if directory was created.
  ///   * Must set `status` to `TF_FAILED_PRECONDITION` if `path` is invalid or
  ///     if it exists but is not a directory.
  ///   * Might use any other error value for `status` to signal other errors.
  ///

// NOTE(allenl): These tests currently go through TFE_Execute and so are
// integration testing rather than purely testing the parallel device. They
// correspond fairly well to the implementation, but testing the C++ directly is
// another option.

namespace tensorflow {
namespace parallel_device {

Variable* Variable::Create(TFE_Context* context, TF_DataType type,
                           const int64_t* dims, const int num_dims,

# addition to this core set of ops, user libraries which are loaded (via
# TF_LoadLibrary/tf.load_op_library) register their ops and kernels with this
# shared object directly.
#
# For example, from Python tf.load_op_library loads a custom op library (via
# dlopen() on Linux), the library finds libtensorflow_framework.so (no

  std::function<void(BackwardFunction*)> backward_function_deleter;
};

// Map from tensor_id to internally-defined operation-id of the operation which
// produced this tensor. A value of -1 means that the tensor was directly
// watched and not the result of any operation in the tape.
using TensorTape = std::unordered_map<int64_t, int64_t>;

// Map from operation-id to tape entry.
template <typename BackwardFunction, typename TapeTensor>

# use the wheel's TensorFlow installation instead of the one made available
# through bazel. This must be done in a different root directory, //bazel_pip/...,
# because "import tensorflow" run from the root directory would instead import
# the folder instead of the venv package.
# 
# Pass --config=pip to run the same suite of tests. If you want to run just one

# script to run pip-compile for each requirement.
# if there is a change in requirements.in then all lock files will be updated
# accordingly

# All commands run relative to this directory
cd "$(dirname "${BASH_SOURCE[0]}")"

mv BUILD.bazel BUILD

SUPPORTED_VERSIONS=("3_9" "3_10" "3_11" "3_12")

for VERSION in "${SUPPORTED_VERSIONS[@]}"
do
  touch "requirements_lock_$VERSION.txt"

# limitations under the License.
# ==============================================================================
set -euxo pipefail

# Run this from inside the tensorflow github directory.
# Usage: setup_venv_test.sh venv_and_symlink_name "glob pattern for one wheel file"
# Example: setup_venv_test.sh bazel_pip "/tf/pkg/*.whl"
# 
# This will create a venv with that wheel file installed in it, and a symlink

# Release jobs are very basic. They don't use any caching or RBE,
# but they do upload logs to resultstore.
# IMPORTANT: trailing slash is required on GCS URIs, as it tells gcloud to
# pretend the path is a directory.
TFCI_ARTIFACT_FINAL_GCS_ENABLE=1
TFCI_ARTIFACT_FINAL_GCS_SA_PATH="${KOKORO_KEYSTORE_DIR}/73361_tensorflow_release_binary_uploader_service_account"
TFCI_ARTIFACT_FINAL_GCS_URI="gs://tensorflow/versions/"

    TF_DeleteDir(dirpath.c_str(), s);
    ASSERT_NE(TF_OK, TF_GetCode(s))
        << "TF_DeleteDir unexpectedly succeeded with a non-empty directory "
        << dirpath;

    TF_DeleteFile(filepath.c_str(), s);
    ASSERT_TF_OK(s) << "TF_DeleteFile failed for " << filepath << ": "
                    << TF_Message(s);

    // Now deleting the directory should work.
    TF_DeleteDir(dirpath.c_str(), s);