*   Being respectful of differing viewpoints and experiences.
*   Gracefully accepting constructive criticism.
*   Focusing on what is best for the community.
*   Showing empathy towards other community members.

Examples of unacceptable behavior by participants include:

*   The use of sexualized language or imagery and unwelcome sexual attention or
    advances.

#define TENSORFLOW_C_C_API_MACROS_INTERNAL_H_

#ifdef __cplusplus
#include "tensorflow/core/platform/status.h"

// Macro to verify that the field `struct_size` of STRUCT_OBJ is initialized.
// `struct_size` is used for struct member compatibility check between core TF
// and plug-ins with the same C API minor version. More info here:
// https://github.com/tensorflow/community/blob/master/rfcs/20200612-stream-executor-c-api/C_API_versioning_strategy.md

#endif  // TF_Bool

// Macro used to calculate struct size for maintaining ABI stability across
// different struct implementations.
#ifndef TF_OFFSET_OF_END
#define TF_OFFSET_OF_END(TYPE, MEMBER) \
  (offsetof(TYPE, MEMBER) + sizeof(((TYPE *)0)->MEMBER))
#endif  // TF_OFFSET_OF_END

# 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)

TFCI_ARTIFACT_LATEST_GCS_URI="gs://tensorflow/nightly/latest/"
TFCI_ARTIFACT_STAGING_GCS_ENABLE=1
TFCI_ARTIFACT_STAGING_GCS_URI="gs://tensorflow-ci-artifact-staging/staging/nightly/$(git rev-parse HEAD)/"

# 2. The internal version numbers get changed
TFCI_NIGHTLY_UPDATE_VERSION_ENABLE=1

# 3. Push results to public build cache (unique cache for MacOS)
if [[ $(uname -s) == "Darwin" ]]; then

      finished_execute_.wait(l);
    }
    if (TF_GetCode(status_.get()) != TF_OK) {
      TF_SetStatus(status, TF_GetCode(status_.get()),
                   TF_Message(status_.get()));
      // Reset the member `status_` so future op executions (after recovery from
      // the bad `status`) start with an OK status.
      TF_SetStatus(status_.get(), TF_OK, "");
    }
    cancellation_manager_ = nullptr;

just getting started, GitHub has a
[how-to](https://help.github.com/articles/using-pull-requests/).

TensorFlow team members will be assigned to review your pull requests. Once the
pull requests are approved and pass continuous integration checks, a TensorFlow
team member will apply `ready to pull` label to your change. This means we are
working on getting your pull request submitted to our internal repository. After

/// that are only valid on random access files have a `TF_RandomAccessFile`
/// argument.
///
/// Lifetime: The wrapper data structures are owned by core TensorFlow. The data
/// pointed to by the `void*` members is always owned by the plugin. The plugin
/// will provide functions to call to allocate and deallocate this data (see
/// next sections) and core TensorFlow ensures to call these at the proper time.
///

          username: _json_key
          password: ${{ secrets.GCP_CREDS }}
      -
        name: Grab the upcoming TF version to tag this container
        run: |
          # [[:digit:]] searches for numbers and \+ joins them together
          major_version=$(grep "^#define TF_MAJOR_VERSION" ./tensorflow/core/public/version.h | grep -o "[[:digit:]]\+")

                                        std::move(mixed_handles), status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK);
  // Can't take the shape of a parallel tensor with varying numbers of axes, but
  // running operations on them is OK.
  TF_ASSERT_OK(unknown_length_vector->Shape(&shape));
  EXPECT_THAT(*shape, ElementsAre(-1));
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> size_op(