namespace tensorflow {

// Abstract interface to a context.
//
// This serves as a factory for creating `AbstractOperation`s and for
// registering traced functions.
// Operations creation within a context can only be executed in that context
// (for now at least).
// Implementations of the context may contain some state e.g. an execution
// environment, a traced representation etc.
class AbstractContext {
 protected:

    validations:
      required: true
  - type: markdown
    attributes:
      value: |
        Please make sure that this is a bug. As per our [GitHub Policy](https://github.com/tensorflow/tensorflow/blob/master/ISSUES.md), we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub.

  - type: dropdown
    id: tf-nightly
    attributes:
      label: Have you reproduced the bug with TensorFlow Nightly?

#       ./any.sh
#       ...
set -euxo pipefail
cd "$(dirname "$0")/../../"  # tensorflow/
# Any request that includes "nightly_upload" should just use the
# local multi-cache (public read-only cache + disk cache) instead.
export TFCI="$(echo $TFCI | sed 's/,nightly_upload/,public_cache,disk_cache/')"
if [[ -n "${TF_ANY_EXTRA_ENV:-}" ]]; then
  export TFCI="$TFCI,$TF_ANY_EXTRA_ENV"
fi

}

# Move into the directory of the script
cd "$(dirname "$0")"

if is_continuous_or_release || [[ -z "$KOKORO_BUILD_ID" ]]; then
  # A continuous job is the only one to publish to latest
  TAG="latest-multi-python"
else
  # If it is a change, grab a good tag for iterative builds
  if [[ -z "${KOKORO_GITHUB_PULL_REQUEST_NUMBER}" ]]; then

# This script uploads all staged artifacts from all previous builds in the same
# job chain to GCS and PyPI.
source "${BASH_SOURCE%/*}/utilities/setup.sh"

# Update the version numbers for Nightly only, then fetch the version numbers
# for choosing the final directory name. This adds "-devYYYYMMDD" to the end of
# the version string (.devYYYYMMDD for Python; see pypi.org/project/tf-nightly)

try:
  files = subprocess.check_output(
      ["grep", "-rlE", '(failures|errors)="[1-9]', sys.argv[1]]
  )
except subprocess.CalledProcessError as e:
  print("No failures found to log!")
  exit(0)

# For test cases, only show the ones that failed that have text (a log)
seen = collections.Counter()
runfiles_matcher = re.compile(r"(/.*\.runfiles/)")


for f in files.strip().splitlines():

  tensorflow::string underlying_device;
  // Set to true whenever a TensorHandle is copied onto the device
  bool* arrived_flag;
  // Set to true whenever an operation is executed
  bool* executed_flag;
  // If true, only explicit op placements are accepted. If false, uses
  // type-based dispatch.
  bool strict_scope_placement;
};

struct LoggedTensor {
  TFE_TensorHandle* tensor;
  LoggedTensor() = delete;