rpm2cpio "devtoolset-10-gcc-10.2.1-11.2.el7.src.rpm" |cpio -idmv
  tar -xvf "gcc-10.2.1-20210130.tar.xz" --strip 1
  ;;
esac

# Apply the devtoolset patches to gcc.
/rpm-patch.sh "gcc.spec"

./contrib/download_prerequisites

mkdir -p "${TARGET}-build"
cd "${TARGET}-build"

"${TARGET}-src/configure" \
      --prefix="${TARGET}/usr" \

  ///        matches any single character (not) on the list
  ///   c: matches character c (c != '*', '?', '\\', '[')
  ///   '\\' c: matches character c
  /// character-range:
  ///   c: matches character c (c != '\\', '-', ']')
  ///   '\\' c: matches character c
  ///   lo '-' hi: matches character c for lo <= c <= hi
  ///
  /// Implementations must allocate `entries` to hold all names that need to be

  if (num_matches >= 0) {
    for (int i = 0; i < num_matches; i++) {
      result->push_back(std::string(matches[i]));
      plugin_memory_free_(matches[i]);
    }
    plugin_memory_free_(matches);
  }

  return StatusFromTF_Status(plugin_status.get());
}

Status ModularFileSystem::DeleteFile(const std::string& fname,

    ```bash
    export flags="--config=opt -k"
    ```

    If the tests are to be run on the GPU, add CUDA paths to LD_LIBRARY_PATH and
    add the `cuda` option flag

    ```bash
    export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH"
    export flags="--config=opt --config=cuda -k"
    ```

    For example, to run all tests under tensorflow/python, do:

}

Status ImmediateExecutionTensorHandle::SummarizeValue(
    std::string& summary) const {
  Status status;
  AbstractTensorPtr resolved(
      // TODO(allenl): Resolve should be const, and the caches that get updated
      // marked mutable.
      const_cast<ImmediateExecutionTensorHandle*>(this)->Resolve(&status));
  if (!status.ok()) {
    return status;
  }
  summary = resolved->SummarizeValue();

updating requirements for multiple minor versions of Python.

It takes in a file with a set of dependencies, and produces a more detailed
requirements file for each version, with hashes specified for each
dependency required, as well as their sub-dependencies.

### How to update/add requirements

By default, the name of the base requirements file is `requirements.in`, but it

    `TFCI` env param pointing to a list of `env` files.
2.  `utilities/setup.sh`, initialized by all top-level scripts, reads and sets
    values from those `TFCI` paths.
    -   `set -a` / `set -o allexport` exports the variables from `env` files so
        all scripts can use them.
    -   `utilities/setup_docker.sh` creates a container called `tf` with all

// TFE_NewTensorHandleFromDeviceMemory.
void DeallocatorWrapperFunc(void* data, size_t len, void* dlmt_vptr) {
  TFE_CallDLManagedTensorDeleter(dlmt_vptr);
}

// Checks whether the stride array matches the layout of compact, row-majored
// data.
bool IsValidStrideCompactRowMajorData(int64_t* shape_arr, int64_t* stride_arr,
                                      int ndim) {
  bool valid = true;

dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0

# 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 ~= 2.17.0.a

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

name: Build SIG Build containers as presubmits

on:
  pull_request:
    types: [labeled, opened, synchronize, reopened]
    paths:
      - '.github/workflows/sigbuild-docker-presubmit.yml'
      - 'tensorflow/tools/tf_sig_build_dockerfiles/**'
      - '!tensorflow/tools/tf_sig_build_dockerfiles/README.md'

permissions:
  contents: read

jobs: