# pretend the path is a directory.
TFCI_ARTIFACT_FINAL_GCS_ENABLE=1
TFCI_ARTIFACT_FINAL_GCS_SA_PATH="${KOKORO_KEYSTORE_DIR}/73361_tensorflow_release_binary_uploader_service_account"
TFCI_ARTIFACT_FINAL_GCS_URI="gs://tensorflow/versions/"
TFCI_ARTIFACT_FINAL_PYPI_ARGS="--config-file=$KOKORO_KEYSTORE_DIR/73361_tensorflow_pypirc_using_global_api_token --repository pypi-warehouse"
TFCI_ARTIFACT_FINAL_PYPI_ENABLE=1

# pretend the path is a directory.
# 1. Upload nightlies
TFCI_ARTIFACT_FINAL_GCS_ENABLE=1
TFCI_ARTIFACT_FINAL_GCS_SA_PATH="${KOKORO_KEYSTORE_DIR}/73361_tensorflow_release_binary_uploader_service_account"
TFCI_ARTIFACT_FINAL_GCS_URI="gs://tensorflow/nightly/"
TFCI_ARTIFACT_FINAL_PYPI_ARGS="--config-file=$KOKORO_KEYSTORE_DIR/73361_tensorflow_pypirc_using_global_api_token --repository pypi-warehouse"
TFCI_ARTIFACT_FINAL_PYPI_ENABLE=1

# for other files to use.
#
# Separately, if TFCI is set *and* there are also additional TFCI_ variables
# set in the shell environment, those variables will be restored after the
# TFCI env has been loaded. This is useful for e.g. on-demand "generic" jobs
# where the user may wish to change just one option.
if [[ -z "${TFCI:-}" ]]; then

    $old_path = [Environment]::GetEnvironmentVariable(\"PATH\", \"Machine\"); \
    [Environment]::SetEnvironmentVariable(\"PATH\", $old_path + \";C:\Program Files\CMake\bin\", \"Machine\");


# Restore default shell for Windows containers.
SHELL ["cmd.exe", "/s", "/c"]

# Default to PowerShell if no other command specified.

        for name in cuda_env_names:
          if name in environ_cp:
            write_action_env_to_bazelrc(name, environ_cp[name])
        break

      # Restore settings changed below if CUDA config could not be validated.
      environ_cp = dict(environ_save)

      set_tf_cuda_version(environ_cp)
      set_tf_cudnn_version(environ_cp)
      if is_windows():

  // Get attr
  AttrValue read_attr;
  GetAttr("test_attr_name", &read_attr);
  ASSERT_EQ(attr.DebugString(), read_attr.DebugString());

  // Retrieve the same attr after save/restore
  Reincarnate();
  AttrValue read_attr2;
  GetAttr("test_attr_name", &read_attr2);
  ASSERT_EQ(attr.DebugString(), read_attr2.DebugString());
}

TEST_F(CApiFunctionTest, Description) {

    *   Provided a new `experimental_skip_saver` argument which, if specified, will suppress the addition of `SavedModel`-native save and restore ops to the `SavedModel`, for cases where users already build custom save/restore ops and checkpoint formats for the model being saved, and the creation of the SavedModel-native save/restore ops simply cause longer model serialization times.

* Add ops to `tensorflow.raw_ops` that were missing.

* `tf.CheckpointOptions`

we don't consider this as a vulnerability, given that models can exhaust
resources in many different ways and solutions exist to prevent this from
happening (e.g., rate limits, ACLs, monitors to restart broken servers).

### Model sharing

If the multitenant design allows sharing models, make sure that tenants and
users are aware of the security risks detailed here and that they are going to