All rights reserved.

Caffe uses a shared copyright model: each contributor holds copyright over
their contributions to Caffe. The project versioning records all such
contribution and copyright details. If a contributor wants to further mark
their specific copyright on a particular contribution, they should indicate
their copyright solely in the commit message of the change when it is
committed.

LICENSE

    Additional reviewers can be added manually based on PR contents
  @param {!object}
    github enables querying for PR and also create issue using rest endpoint
    context has the commit message details in the payload
  @return {string} Returns the message with labels attached and assignees added
*/
const filter_action = async ({github, context, domain}) => {
  const labels = ['kokoro:force-run'];

  let assignees = [];

  Created issue is assigned to original PR owner and reviewer.

  @param {!object}
    github enables querying for PR and also create issue using rest endpoint
    context has the commit message details in the payload
  @return {string} Returns the issue number and title
*/
module.exports = async ({github, context}) => {
  const rollback_commit = context.payload.head_commit.id;

    which is a NumPy-compatible API for writing TF programs. See the
    [detailed guide](https://www.tensorflow.org/guide/tf_numpy) to learn more.
    Additional details below.

*   Adds Support for
    [TensorFloat-32](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/)
    on Ampere based GPUs. TensorFloat-32, or TF32 for short, is a math mode for

    echo 'behavior, create a new shell.'
    cat "$FROM_ENV"
    source "$FROM_ENV"
    rm "$FROM_ENV"
  fi
fi

# Mac builds have some specific setup needs. See setup_macos.sh for details
if [[ "${OSTYPE}" =~ darwin* ]]; then
  source ./ci/official/utilities/setup_macos.sh
fi

# Create and expand to the full path of TFCI_OUTPUT_DIR
export TFCI_OUTPUT_DIR=$(realpath "$TFCI_OUTPUT_DIR")

# limitations under the License.
# ==============================================================================

# Builds the following Docker images for Linux ARM64. See the accompanying
# Dockerfile for more details:
# - gcr.io/tensorflow-sigs/build-arm64:jax-latest-multi-python
# - gcr.io/tensorflow-sigs/build-arm64:tf-latest-multi-python

set -exo pipefail

function is_continuous_or_release() {

/// by both core TensorFlow and the plugin at load time. We don't include this
/// data in the structures here to prevent cases when padding/packing enabled by
/// different compiler options breaks compatibility. For more details about how
/// this is used, please consult next sections. Here we just wrap these tables
/// in lint warnings so that changes here cause changes to the versioning data

// (1) TF_Output or TFE_TensorHandle* as arguments to TF_AddInput,
//     TF_AddInputList
// (2) TF_ColocateWith, TF_AddControlInput etc. do not make sense.
// (3) Implementation detail: Avoid use of NodeBuilder/NodeDefBuilder since
//     the additional sanity checks there seem unnecessary;
typedef struct TFE_Op TFE_Op;

TF_CAPI_EXPORT extern TFE_Op* TFE_NewOp(TFE_Context* ctx,

set -euxo pipefail

function resultstore_extract_fallback {
  # In case the main script fails somehow.
  cat <<EOF
IMPORTANT: For bazel invocations that uploaded to ResultStore (e.g. RBE), you
can view more detailed results that are probably easier to read than this log.
Try the links below:
EOF
  # Find any "Streaming build results to" line, then print the last word in it,
  # and don't print duplicates

      failure.text = failure_msg
    else:
      properties = ElemTree.SubElement(testcase, 'properties')
      success_msg = 'Build completed successfully.\n' f'See {url} for details.'
      ElemTree.SubElement(properties, 'property',
                          name='description',
                          value=success_msg)
      if command:
        ElemTree.SubElement(properties, 'property',