docker container prune -f
          docker image prune -af
      - name: Clean repository
        shell: bash
        run: find /home/ubuntu/actions-runner/_work/tensorflow/tensorflow/. -name . -o -prune -exec sudo rm -rf -- {} + || true
      - name: Checkout repository for nightly (skipped for releases)
        if: ${{ github.event_name == 'schedule' }}
        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2

#       export TF_ANY_TARGETS="quoted list of targets, like on the command line"
#       export TF_ANY_MODE="test" or "build" or "run" (default: "test")
#       ./any.sh
#
# 2. RUN ANY OTHER SCRIPT AND ENV WITH NO SIDE EFFECTS (NO UPLOADS)
#    To use:
#       export TFCI=ci/official/envs/env_goes_here
#       export TF_ANY_SCRIPT=ci/official/wheel.sh
#       ./any.sh
#
# 3. DO THE SAME WITH A LOCAL CACHE OR RBE:

type ConditionOperand struct {
	Operand      *Operand      `parser:"@@"`
	ConditionRHS *ConditionRHS `parser:"@@?"`
}

// ConditionRHS represents the right-hand-side of Compare, Between, In
// or Like expressions.
type ConditionRHS struct {
	Compare *Compare `parser:"  @@"`
	Between *Between `parser:"| @@"`
	In      *In      `parser:"| \"IN\" @@"`

          docker container prune -f
          docker image prune -af
      - name: Clean repository
        shell: bash
        run: find /home/ubuntu/actions-runner/_work/tensorflow/tensorflow/. -name . -o -prune -exec sudo rm -rf -- {} + || true
      - name: Checkout repository for nightly (skipped for releases)
        if: ${{ github.event_name == 'schedule' }}
        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2

	sealedKey := objectKey.Seal(key.Plaintext, crypto.GenerateIV(rand.Reader), crypto.S3.String(), bucket, "")
	crypto.S3.CreateMetadata(metadata, key.KeyID, key.Ciphertext, sealedKey)
	_, err = sio.Encrypt(outbuf, bytes.NewBuffer(input), sio.Config{Key: objectKey[:], MinVersion: sio.Version20, CipherSuites: fips.DARECiphers()})
	if err != nil {
		return output, metabytes, err
	}
	metabytes, err = json.Marshal(metadata)
	if err != nil {
		return
	}

       * instance.
       */
      super("");
    }

    @Override
    Comparable<?> endpoint() {
      throw new IllegalStateException("range unbounded on this side");
    }

    @Override
    boolean isLessThan(Comparable<?> value) {
      return true;
    }

    @Override
    BoundType typeAsLowerBound() {
      throw new IllegalStateException();
    }

                    }
                    callback.iterate(accessResult);
                }

                final String sid = scrollId;
                response = getClient().get(c -> c.prepareSearchScroll(sid).setScroll(new TimeValue(scrollTimeout)).execute());
                if (!scrollId.equals(response.getScrollId())) {
                    getClient().clearScroll(scrollId);

          fi
          docker container prune -f
      - name: Clean repository
        shell: bash
        run: find /home/ubuntu/actions-runner/_work/tensorflow/tensorflow/. -name . -o -prune -exec sudo rm -rf -- {} + || true
      - name: Checkout repository for nightly (skipped for releases)
        if: ${{ github.event_name == 'schedule' }}
        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2

  an older version you must use an `S3 API client` such as [`mc`](https://github.com/minio/mc).

- All features currently used by your buckets will work as is without any changes
  - SSE (Server Side Encryption)
  - Replication (Server Side Replication)

## Prerequisites

- It is assumed you have users created and configured with relevant access policies, to start with

- Parquet API supports columnar compression for  using GZIP, Snappy, LZ4. Whole object compression is not supported for Parquet objects.
- Server-side encryption - The Select API supports querying objects that are protected with server-side encryption.