TF_GUARDED_BY(mu);
  bool delete_requested TF_GUARDED_BY(mu);  // set true by TF_DeleteGraph

  // Used to link graphs contained in TF_WhileParams to the parent graph that
  // will eventually contain the full while loop.
  TF_Graph* parent;
  TF_Output* parent_inputs;
};

struct TF_OperationDescription {
  TF_OperationDescription(TF_Graph* g, const char* op_type,

#   and scripts on Linux create a persistent container called "tf"
#   which mounts your TensorFlow directory into the container.
#
#   Important: because the container is persistent, you cannot change TFCI
#   variables in between script executions. To forcibly remove the
#   container and start fresh, run "docker rm -f tf". Removing the container
#   destroys some temporary bazel data and causes longer builds.
#

                          ImmediateExecutionTensorHandle* input) = 0;

  virtual ImmediateExecutionContext* GetContext() const = 0;

  // Following two methods are used to support custom device.
  // Return true if the inputs contain custom device tensor handle. It means
  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

  virtual const tensorflow::OpDef* OpDef() const = 0;

    const std::string translated_name =
        tensorflow::io::JoinPath(root_dir_, path);
    // We have already checked `GetParam().empty()` in
    // `ModularFileSystemTest()`. root_dir_ should contain `GetParam() + "://"`
    // if it isn't empty.
    return translated_name;
  }

  // Converts absolute paths to paths relative to root_dir_.
  StringPiece GetRelativePath(StringPiece absolute_path) {

TF_CAPI_EXPORT extern PJRT_Client* TF_GetPjRtCClient(const char* device_type,
                                                     TF_Status* status);

// Gets the `PJRT_Buffer*` stored in the tensor. The status will contain error
// if the tensor does not have a `PjRtCApiBuffer`.
TF_CAPI_EXPORT extern PJRT_Buffer* TF_GetPjRtCBuffer(TF_Tensor* c_tensor,
                                                     TF_Status* status);

Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst);

namespace {

static void ExpectHasSubstr(StringPiece s, StringPiece expected) {
  EXPECT_TRUE(absl::StrContains(s, expected))
      << "'" << s << "' does not contain '" << expected << "'";
}

// Returns the GPU device name if there is one (with arbitrary tie breaking if
// there are more than one), or "" otherwise.
string GPUDeviceName(TF_Session* session) {

# targets matching the provided query, which start with // or @ but not
# //tensorflow (so it looks for //third_party, //external, etc.), and then
# gathers the list of all packages (i.e. directories) which contain those
# targets.
license_query() {
 bazel cquery --experimental_cc_shared_library "$1" --keep_going \
  | grep -e "^//" -e "^@" \
  | grep -E -v "^//tensorflow" \
  | sed -e 's|:.*||' \
  | sort -u
}

EOF
  exit 1
fi
TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX"

# These flags share the user's gcloud credentials with the container, so that bazel
# inside the container can authenticate. Note: TF's CI does not have any credential
# stored here.

          if [[ $running_containers == "" ]]; then
            echo "No running containers";
          else
            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f
      - name: Clean repository
        shell: bash

"tf.foo" being imported first.

The `_api/v2/api_packages.txt` file is generated during the TensorFlow API v2
init files creation process and is subsequently stored in the wheel file after
the build. It also contains a few paths that cannot be directly imported. These
paths point to attributes or sub-modules within a module's namespace, but they
don't correspond to an actual file or directory on the filesystem. The list of