#   export TF_BISECT_GOOD=a_good_commit_sha
#   export TF_BISECT_BAD=a_failing_commit_sha
#   export TF_ANY_TARGETS="quoted list of targets, like on the command line"
#   export TF_ANY_MODE=test
set -euxo pipefail
cd "$(dirname "$0")/../../"  # tensorflow/
export TFCI="$(echo $TFCI | sed 's/,nightly_upload/,public_cache,disk_cache/')"
git bisect start "$TF_BISECT_BAD" "$TF_BISECT_GOOD"

    rare occasion we may reject it.

**3. Review**

-   For a valid PR, reviewer (person familiar with the code/functionality)
    checks if the PR looks good or needs additional changes.
-   If all looks good, the reviewer will approve the PR.
-   If a change is needed, the contributor is requested to make the suggested
    change.
-   You make the change and submit it for the review again.

    which is a NumPy-compatible API for writing TF programs. This module
    provides class `ndarray`, which mimics the `ndarray` class in NumPy, and
    wraps an immutable `tf.Tensor` under the hood. A subset of NumPy functions
    (e.g. `numpy.add`) are provided. Their inter-operation with TF facilities is
    seamless in most cases. See

          close-issue-reason: completed
          # List of labels to remove when issues/PRs unstale. 
          labels-to-remove-when-unstale: "stat:contribution welcome,stat:good first issue"
          any-of-labels: "stat:contribution welcome,stat:good first issue"
          stale-issue-message: > 
            This issue is stale because it has been open for 180 days with no activity.

# Linux builds. Instructions are available in the official documentation:
# https://www.tensorflow.org/install/source#install_clang_recommended_linux_only
# Another good option is to use our Docker containers to build and test TF:
# https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/tf_sig_build_dockerfiles.
build:unsupported_cpu_linux --config=avx_linux