TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_macos_x86"
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=macos_x86
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-darwin-x86_64"
TFCI_MACOS_BAZEL_TEST_DIR_ENABLE=1
TFCI_MACOS_BAZEL_TEST_DIR_PATH="/System/Volumes/Data/bazel_output"
TFCI_MACOS_INSTALL_BAZELISK_ENABLE=1

type lcEventSrc uint8

//revive:disable:var-naming Underscores is used here to indicate where common prefix ends and the enumeration name begins
const (
	lcEventSrc_None lcEventSrc = iota
	lcEventSrc_Heal
	lcEventSrc_Scanner
	lcEventSrc_Decom
	lcEventSrc_Rebal
	lcEventSrc_s3HeadObject
	lcEventSrc_s3GetObject
	lcEventSrc_s3ListObjects
	lcEventSrc_s3PutObject
	lcEventSrc_s3CopyObject

#   TF_BISECT_BAD: First bad commit (e.g. commit from the first failing job)
#   TF_BISECT_SCRIPT: The build script path relative to the TF root dir, e.g.
#     ci/official/wheel.sh
#   TFCI: The env config path, relative to the TF root dir, e.g.
#     ci/official/envs/an_env_config
#
# Note that you can combine bisect.sh with any.sh to bisect a single test:
#
#   export TFCI=...

Most platforms typically build on the features of other platforms.

By understanding the Gradle platforms and their relationships, you can get a feel for where in the Gradle source a particular feature might be implemented.

See [Gradle platform architecture](platforms.md) for a list of the platforms and more details.

## Gradle runtimes

				return serr
			}
			return nil
		}, index)
	}

	errs := g.Wait()

	// Initialize heal result info
	res = madmin.HealResultItem{
		Type:      madmin.HealItemBucket,
		Bucket:    bucket,
		DiskCount: len(localDrives),
		SetCount:  -1, // explicitly set an invalid value -1, for bucket heal scenario
	}

	// mutate only if not a dry-run
	if opts.DryRun {
		return res, nil
	}

# odd to attempt to validate the quality of the pip package. The wheel is
# installed into a virtual environment, and then that venv is used to run all
# bazel tests with a special flag "--define=no_tensorflow_py_deps=true", which
# drops all the bazel dependencies for each py_test; this makes all the tests
# use the wheel's TensorFlow installation instead of the one made available

#       ./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:
#       export TF_ANY_EXTRA_ENV=ci/official/envs/public_cache,ci/official/envs/disk_cache
#       ...
#       ./any.sh
#     or

		}

		bytesWritten += n
	}

	if bytesWritten != length {
		return bytesWritten, errLessData
	}

	return bytesWritten, derr
}

// Heal reads from readers, reconstruct shards and writes the data to the writers.
func (e Erasure) Heal(ctx context.Context, writers []io.Writer, readers []io.ReaderAt, totalLength int64, prefer []bool) (derr error) {
	if len(writers) != e.parityBlocks+e.dataBlocks {

			// If we have successfully written all the content that was asked
			// by the client, but we still see an error - this would mean
			// that we have some parts or data blocks missing or corrupted
			// - attempt a heal to successfully heal them for future calls.
			if written == partLength {
				var scan madmin.HealScanMode
				switch {
				case errors.Is(err, errFileNotFound):
					scan = madmin.HealNormalScan

      with:
        python-version: "3.9"
    - name: Install Python dependencies
      run: |
        python -m pip install --upgrade pip
        pip install pylint==2.13.9 numpy wheel
    - name: Run PyLint on changed files
      run: |