// Make a copy of the supplied metadata to avoid
	// to change the original one.
	defaultMeta := make(map[string]string, len(userMeta))
	for k, v := range userMeta {
		// skip tier metadata when copying metadata from source object
		switch k {
		case metaTierName, metaTierStatus, metaTierObjName, metaTierVersionID:
			continue
		}
		defaultMeta[k] = v
	}

	type healResp struct {
		respBytes []byte
		apiErr    APIError
		errBody   string
	}

	// Define a closure to start sending whitespace to client
	// after 10s unless a response item comes in
	keepConnLive := func(w http.ResponseWriter, r *http.Request, respCh chan healResp) {
		ticker := time.NewTicker(time.Second * 10)
		defer ticker.Stop()
		started := false
	forLoop:
		for {

  |   what is allowed in the field.
  |
  | o validators: there could be several levels of validation. Simple type validation could
  |   be done with a regex, but we need inter-field validation and rules which could be
  |   dealt with by something like drools.
  |
  | o i18n: would be good to be able to have names/descriptions/specifications
  |   in as many languages as possible. (see MNG-3626)
  |

must either all be present or all be absent. If present, each name
stands for one item (parameter or result) of the specified type and
all non-<a href="#Blank_identifier">blank</a> names in the signature
must be <a href="#Uniqueness_of_identifiers">unique</a>.
If absent, each type stands for one item of that type.
Parameter and result
lists are always parenthesized except that if there is exactly

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    provides class `ndarray`, which mimics the `ndarray` class in NumPy, and
    wraps an immutable `tf.Tensor` under the hood. A subset of NumPy functions
    (e.g. `numpy.add`) are provided. Their inter-operation with TF facilities is
    seamless in most cases. See
    [tensorflow/python/ops/numpy_ops/README.md](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/numpy_ops/README.md)