/**
     * Gets or creates an SMB session for the specified target.
     *
     * @param tf the CIFS context
     * @param targetHost the target host name
     * @param targetDomain the target domain
     * @return session
     */
    SmbSession getSmbSession(CIFSContext tf, String targetHost, String targetDomain);

    /**
     * Checks if this transport uses the SMB2 protocol.
     *

TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build-arm64:latest
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--target=tf ci/official/containers/ml_build_arm64"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-linux-arm64"
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_AUDIT_ENABLE=1
TFCI_WHL_AUDIT_PLAT=manylinux2014_aarch64
TFCI_WHL_BAZEL_TEST_ENABLE=1

# ==============================================================================
"""
Top-level module of TensorFlow. By convention, we refer to this module as
`tf` instead of `tensorflow`, following the common practice of importing
TensorFlow via the command `import tensorflow as tf`.

The primary function of this module is to import all of the public TensorFlow
interfaces into a single place. The interfaces themselves are located in

#
# ==============================================================================

# Do not print anything if this is not being used interactively
[ -z "$PS1" ] && return

# Set up attractive prompt
export PS1="\[\e[31m\]tf-docker\[\e[m\] \[\e[33m\]\w\[\e[m\] > "
export TERM=xterm-256color
alias grep="grep --color=auto"
alias ls="ls --color=auto"
# Fix nvidia-docker

load("@python//:defs.bzl", "compile_pip_requirements")
load("@python_version_repo//:py_version.bzl", "REQUIREMENTS")

# TODO(ybaturina): Remove once TF is migrated to CUDA 12.9.
genrule(
    name = "nvidia_constraints",
    srcs = ["nvidia-requirements.txt"],
    outs = ["nvidia-constraints.txt"],
    cmd = """sed -E "s/>=/==/" $(location nvidia-requirements.txt) > $@;""",

    ```

    This will run it using your installed version of TensorFlow. To be sure
    you're running the same code that you're testing:

    *   Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/)
        `pip install -U tf-nightly`
    *   Rebase your pull request onto a recent pull from
        [TensorFlow's](https://github.com/tensorflow/tensorflow) master branch.

  PROFILE_JSON_PATH="$PROFILE_JSON_PATH/profile.json.gz"
else
  PROFILE_JSON_PATH="$TFCI_OUTPUT_DIR/profile.json.gz"
fi

# TODO(b/361369076) Remove the following block after TF NumPy 1 is dropped
# Move hermetic requirement lock files for NumPy 1 to the root
if [[ "$TFCI_WHL_NUMPY_VERSION" == 1 ]]; then
  cp ./ci/official/requirements_updater/numpy1_requirements/*.txt .
fi

  fun logHandler() =
    ConsoleHandler().apply {
      level = Level.FINE
      formatter =
        object : SimpleFormatter() {
          override fun format(record: LogRecord) = String.format("[%1\$tF %1\$tT] %2\$s %n", record.millis, record.message)
        }
    }

  fun enable(
    loggerClass: String,
    handler: Handler = logHandler(),
  ): Closeable {
    val logger = Logger.getLogger(loggerClass)

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
#
# Sets up custom apt sources for our TF images.

# Prevent apt install tzinfo from asking our location (assumes UTC)
export DEBIAN_FRONTEND=noninteractive

# Set up shared custom sources
apt-get update
apt-get install -y gnupg ca-certificates

from anywhere, and executes the graphs it is sent without performing any checks.
Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can