#
# testcase: "a:b c:d"
# testcase: "a b:c d:e f"
# testcase: "a b:c:d e"
parse_KUBECONFIG () {
TMPDIR=""
if [[ "$1" =~ ([^:]*):(.*) ]]; then
  while true; do
    rematch=${BASH_REMATCH[1]}
    add_KUBECONFIG_if_exists "$rematch"
    remainder="${BASH_REMATCH[2]}"
    if [[ ! "$remainder" =~ ([^:]*):(.*) ]]; then
      if [[ -n "$remainder" ]]; then
        add_KUBECONFIG_if_exists "$remainder"
        break

        "//waymo/ml/compiler/mlir/...",
        # Allow visibility from the mlir language server.
        "//learning/brain/mlir/mlir_lsp_server/...",
        "//research/language_modeling/sentence_explorer/ondevice/...",
        "//learning/brain/research/babelfish/inference/speech_tflite/mlir/...",
    ],
)

td_library(
    name = "tensorflow_lite_ops_td_files",
    srcs = [
        "ir/tfl_op_enums.td",

	if _, ok := metadata[strings.ToLower(xhttp.ContentType)]; !ok {
		metadata[strings.ToLower(xhttp.ContentType)] = "binary/octet-stream"
	}

	// https://github.com/google/security-research/security/advisories/GHSA-76wf-9vgp-pj7w
	for k := range metadata {
		if equals(k, xhttp.AmzMetaUnencryptedContentLength, xhttp.AmzMetaUnencryptedContentMD5) {
			delete(metadata, k)
		}
	}

		lastCheckpoint = st.Object
	}
	workers := make([]chan ReplicateObjectInfo, resyncParallelRoutines)
	resultCh := make(chan TargetReplicationResyncStatus, 1)
	defer xioutil.SafeClose(resultCh)
	go func() {
		for r := range resultCh {
			s.incStats(r, opts)
			globalSiteResyncMetrics.updateMetric(r, opts.resyncID)
		}
	}()

	var wg sync.WaitGroup

	jobType := r.Form.Get("jobType")
	if jobType == "" {
		jobType = string(madmin.BatchJobReplicate)
	}

	resultCh := make(chan itemOrErr[ObjectInfo])

	ctx, cancel := context.WithCancel(ctx)
	defer cancel()

	if err := objectAPI.Walk(ctx, minioMetaBucket, batchJobPrefix, resultCh, WalkOptions{}); err != nil {
		writeErrorResponseJSON(ctx, w, toAPIError(ctx, err), r.URL)
		return
	}

	//    from w2 ResultCh it could have happened that its buffer was also
	//    filling in and given we no longer had timeBudget (consumed in (1))
	//    trying to put next item was simply breaking the watch
	// Using fakeTimeBudget gives us always a budget to wait and have a test
	// pick up something from ResultCh in the meantime.
	cacher.dispatchTimeoutBudget = &fakeTimeBudget{}

			// Do not need to send any internal metadata
			// values to client.
			toRemove = append(toRemove, k)
			continue
		}

		// https://github.com/google/security-research/security/advisories/GHSA-76wf-9vgp-pj7w
		if equals(k, xhttp.AmzMetaUnencryptedContentLength, xhttp.AmzMetaUnencryptedContentMD5) {
			toRemove = append(toRemove, k)
			continue
		}
	}

            which drops erroneous elements from `Dataset`s.
    *   Added
        [`tf.data.Dataset.rebatch`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#rebatch),
        a new API for rebatching the elements of a dataset.

*   `tf.distribute`:

    *   Added