/**
     * @param ctx
     * @return session
     */
    SmbSession getSmbSession ( CIFSContext ctx );


    /**
     * @param tf
     * @param targetHost
     * @param targetDomain
     * @return session
     */
    SmbSession getSmbSession ( CIFSContext tf, String targetHost, String targetDomain );


    /**
     * @return whether this is a SMB2 connection
     * @throws SmbException
     */

  // in current TF runtime. For tfrt, it is used by fallback op handler.
  virtual void StartStep() = 0;
  // Destroy the step resource container for a training step.
  virtual void EndStep() = 0;

  // Return the Eager Executor for current thread. Please note that Eager
  // Executor is only used in current TF but not in TFRT.
  virtual EagerExecutor& Executor() = 0;

  fi
fi

AR_IMAGE_PATH="us-central1-docker.pkg.dev/tensorflow-sigs/tensorflow/build-arm64"

# Build for both JAX and TF usage.  We do these in one place because they share
# almost all of the same cache layers
export DOCKER_BUILDKIT=1
for target in jax tf; do
  AR_IMAGE="$AR_IMAGE_PATH:$target-$TAG"
  docker pull "$AR_IMAGE" || true
  # Due to some flakiness of resources pulled in the build, allow the docker

RUN groupadd -g 1001 buildslave && useradd -m -u 1001 -g buildslave buildslave
RUN mkdir -p /tf/venv
RUN chown -R buildslave:buildslave /tf
RUN dpkg -i /patchelf/patchelf_0.14.3-1_arm64.deb

# All lines past this point are reset when $CACHEBUSTER is set. We need this
# for Python specifically because we install some nightly packages which are

typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.4.0, < 0.5.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
keras-nightly ~= 3.0.0.dev
tb-nightly ~= 2.19.0.a
# Test dependencies

		"1.21")

	assert.Equal(t, expectedOutput, messages[0])
}

func init() {
	cli.MakeKubeFactory = func(k kube.CLIClient) cmdutil.Factory {
		tf := cmdtesting.NewTestFactory()
		_, _, codec := cmdtesting.NewExternalScheme()
		tf.UnstructuredClient = &fake.RESTClient{
			NegotiatedSerializer: resource.UnstructuredPlusDefaultContentConfig().NegotiatedSerializer,
			Resp: &http.Response{

import jcifs.internal.Request;


/**
 * @author mbechler
 * @internal
 */
public interface SmbTreeInternal extends SmbTree {

    /**
     * @param tf
     * @throws SmbException
     */
    @Deprecated
    void connectLogon ( CIFSContext tf ) throws SmbException;


    /**
     * @param request
     * @param params
     * @return response message
     * @throws CIFSException
     */

_current_module.app.flags = flags  # pylint: disable=undefined-variable
setattr(_current_module, "flags", flags)

_major_api_version = 1

# Add module aliases from Keras to TF.
# Some tf endpoints actually lives under Keras.
_current_module.layers = _KerasLazyLoader(
    globals(),
    submodule="__internal__.legacy.layers",
    name="layers",
    mode="v1")
if _tf_uses_legacy_keras:

		}
	}
}

func init() {
	cli.MakeKubeFactory = func(k kube.CLIClient) cmdutil.Factory {
		tf := cmdtesting.NewTestFactory()
		_, _, codec := cmdtesting.NewExternalScheme()
		tf.UnstructuredClient = &fake.RESTClient{
			NegotiatedSerializer: resource.UnstructuredPlusDefaultContentConfig().NegotiatedSerializer,
			Resp: &http.Response{

// On success, dequeues a tensor from a TF-managed FifoQueue given by
// `tensor_id`, associated with `session`. There must be a graph node named
// "fifo_queue_dequeue_<tensor_id>", to be executed by this API call.

// Caller must call TF_DeleteTensor() over the returned tensor. If the queue is
// empty, this call is blocked.
//
// Tensors are enqueued via the corresponding TF enqueue op.