if (fs == nullptr || !s.ok())
      GTEST_SKIP() << "No filesystem registered: " << s;

    s = fs->CreateDir(root_dir_);
    if (!s.ok()) {
      GTEST_SKIP() << "Cannot create working directory: " << s;
    }
  }

  // Converts path reference to URI reference.
  //
  // If URI scheme is empty, URI reference is `path` relative to current test

    given-names: Matthieu
  - family-names: Ghemawat
    given-names: Sanjay
  - family-names: Goodfellow
    given-names: Ian
  - family-names: Harp
    given-names: Andrew
  - family-names: Irving
    given-names: Geoffrey
  - family-names: Isard
    given-names: Michael
  - family-names: Jozefowicz
    given-names: Rafal
  - family-names: Jia
    given-names: Yangqing
  - family-names: Kaiser

team member will apply `ready to pull` label to your change. This means we are
working on getting your pull request submitted to our internal repository. After
the change has been submitted internally, your pull request will be merged
automatically on GitHub.

If you want to contribute, start working through the TensorFlow codebase,
navigate to the

ls build_output
```

## Contribution & Maintenance

The TensorFlow team does not yet have guidelines in place for contributing to
this directory. We are working on it. Please join a TF SIG Build meeting (see:
bit.ly/tf-sig-build-notes) if you'd like to discuss the future of contributions.

### Brief System Overview

  paths = glob.glob('**/third_party/gpus/find_cuda_config.py', recursive=True)
  if not paths:
    raise FileNotFoundError(
        "Can't find 'find_cuda_config.py' script inside working directory")
  proc = subprocess.Popen(
      [environ_cp['PYTHON_BIN_PATH'], paths[0]] + cuda_libraries,
      stdout=subprocess.PIPE,
      env=maybe_encode_env(environ_cp))

  if proc.wait():

// tf.device("CUSTOM"):` from Python if `device_name` for this call is
// "/job:localhost/replica:0/task:0/device:CUSTOM:0".
//
// The custom device defines copy operations for moving TensorHandles on and
// off, and an execution operation for named operations. Often execution will
// simply wrap op execution on one or more physical devices.
//

# Using TensorFlow Securely

This document discusses the TensorFlow security model. It describes the security
risks to consider when using models, checkpoints or input data for training or
serving. We also provide guidelines on what constitutes a vulnerability in
TensorFlow and how to report them.

This document applies to other repositories in the TensorFlow organization,
covering security practices for the entirety of the TensorFlow ecosystem.

if [[ "${TFCI_MACOS_UPGRADE_PYENV_ENABLE}" == 1 ]]; then
  # The TFCI Mac VM image seems to have uncommitted local changes to the Pyenv
  # repository so we have to discard them and reset the working directory before
  # we can pull in the latest changes.
  cd /Users/kbuilder/.pyenv/ && git reset --hard HEAD && git pull && cd -
fi

# "TFCI_MACOS_PYENV_INSTALL_ENABLE" controls whether to use Pyenv to install

        "//learning/brain/mlir/...",
        "//learning/brain/tfrt/...",
        "//learning/lib/ami/simple_ml/...",
        "//learning/pathways/...",
        "//learning/serving/contrib/tfrt/mlir/canonical_ops/...",
        "//learning/serving/experimental/remote_predict/...",
        "//perftools/accelerators/xprof/convert/...",
        "//perftools/accelerators/xprof/integration_tests/...",

*   `Estimator.export_savedmodel()` now includes all valid serving signatures
    that can be constructed from the Serving Input Receiver and all available
    ExportOutputs. For instance, a classifier may provide regression- and
    prediction-flavored outputs, in addition to the classification-flavored one.
    Building signatures from these allows TF Serving to honor requests using the