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
sub-modules, as described below.

Note that the file `__init__.py` in the TensorFlow source code tree is actually
only a placeholder to enable test cases to run. The TensorFlow build replaces

Instead, an independent Python toolchain is registered, ensuring the right
dependencies are always used. \
See https://github.com/bazelbuild/rules_python/ for more details.

### Specifying the Python version

Note: Only a number of minor Python versions are supported at any given time.

By default, the lowest supported version is used.

To set a different version, use the `TF_PYTHON_VERSION` environment variable,
e.g.

```

ln -s "/usr/include/aarch64-linux-gnu/asm" "${TARGET}/usr/include/asm"

# Download glibc's shared and development libraries based on the value of the
# `VERSION` parameter.
# Note: 'Templatizing' this and the other conditional branches would require
# defining several variables (version, os, path) making it difficult to maintain
# and extend for future modifications.
mkdir -p glibc-src
mkdir -p glibc-build

***tensorflow/c/experimental/ops/\*_ops.{cc,h}***.

Run this `update_cpp_ops.sh` script when Op definitions change in the registry.

To generate additional operators, extend the lists in this script. Note that
category names correspond to generated source file names, and should be
consistent with the original source files registering each operator. For example

  void LookupOrCompute(const std::string& key, T* value,
                       const ComputeFunc& compute_func, TF_Status* status) {
    if (max_age_ == 0) {
      return compute_func(key, value, status);
    }

    // Note: we hold onto mu_ for the rest of this function. In practice, this
    // is okay, as stat requests are typically fast, and concurrent requests are
    // often for the same file. Future work can split this up into one lock per

struct TFE_Executor {
  explicit TFE_Executor(bool async, bool enable_streaming_enqueue,
                        int in_flight_nodes_limit)
      : owned_executor(new tensorflow::EagerExecutor(
            async, enable_streaming_enqueue, in_flight_nodes_limit)) {}

  explicit TFE_Executor(tensorflow::EagerExecutor* executor)
      : owned_executor(nullptr), unowned_executor(executor) {}

astunparse == 1.6.3
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

# limitations under the License.
# ==============================================================================
# Sourcing this enables Bazel remote cache (read and write)
# Note that "_push" cache configs write to GCS buckets and require
# authentication. If you are not a Googler, source "public_cache" to enable the
# public read-only cache.
# The cache configs are different for MacOS and Linux

else
   tfrun bazel test $TFCI_BAZEL_COMMON_ARGS --profile "$TFCI_OUTPUT_DIR/profile.json.gz" --config="${TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX}_pycpp_test"
fi

# Note: the profile can be viewed by visiting chrome://tracing in a Chrome browser.
# See https://docs.bazel.build/versions/main/skylark/performance.html#performance-profiling

tfrun ./ci/official/utilities/repack_libtensorflow.sh "$TFCI_OUTPUT_DIR" "$TFCI_LIB_SUFFIX"

if [[ "$TFCI_ARTIFACT_STAGING_GCS_ENABLE" == 1 ]]; then
  # Note: -n disables overwriting previously created files.
  gsutil cp "$TFCI_OUTPUT_DIR"/*.tar.gz "$TFCI_ARTIFACT_STAGING_GCS_URI"