return nil, toAdminAPIErr(h.ctx, err), ""
	}
	return b, noError, ""
}

// PopHealStatusJSON - Called by heal-status API. It fetches the heal
// status results from global state and returns its JSON
// representation. The clientToken helps ensure there aren't
// conflicting clients fetching status.
func (ahs *allHealState) PopHealStatusJSON(hpath string,
	clientToken string) ([]byte, APIErrorCode,
) {

 * instances; usually this is [OkHttpClient].
 *
 * ## Web Socket Lifecycle
 *
 * Upon normal operation each web socket progresses through a sequence of states:
 *
 *  * **Connecting:** the initial state of each web socket. Messages may be enqueued but they won't
 *    be transmitted until the web socket is open.
 *

                if (!responseData.getMimeType().matches(mimeTypeCondition)) {
                    return false;
                }
            }

            // Check status code condition
            String statusCodeCondition = conditions.get("statusCode");
            if (statusCodeCondition != null) {
                int expectedCode = Integer.parseInt(statusCodeCondition);

xmlns="http://s3.amazonaws.com/doc/2006-03-01/"><Role></Role><Rule><Status>Enabled</Status><DeleteMarkerReplication><Status>Disabled</Status></DeleteMarkerReplication><DeleteReplication><Status>Disabled</Status></DeleteReplication><Prefix>key-prefix</Prefix><Destination><Bucket>arn:minio:replication:::destinationbucket</Bucket></Destination></Rule><Rule><Status>Enabled</Status><Priority>3</Priority><DeleteMarkerReplication><Status>Disabled</Status></DeleteMarkerReplication><DeleteReplication><Status>Disab...

}

void TF_TensorFromProto(const TF_Buffer* from, TF_Tensor* to,
                        TF_Status* status) {
  TF_SetStatus(status, TF_OK, "");
  tensorflow::TensorProto from_tensor_proto;
  status->status = BufferToMessage(from, &from_tensor_proto);
  if (!status->status.ok()) {
    return;
  }
  status->status =
      tensorflow::down_cast<tensorflow::TensorInterface*>(to->tensor)
          ->FromProto(from_tensor_proto);

  status->status = c.construction_status();
  if (!status->status.ok()) return;

  if (op_reg_data->shape_inference_fn == nullptr) {
    status->status =
        InvalidArgument("No shape inference function exists for op '",
                        node_def.op(), "', did you forget to define it?");
    return;
  }

  status->status = c.Run(op_reg_data->shape_inference_fn);

xmlns="http://s3.amazonaws.com/doc/2006-03-01/"><Role>arn:aws:iam::AcctID:role/role-name</Role><Rule><Status>Enabled</Status><DeleteMarkerReplication><Status>Disabled</Status></DeleteMarkerReplication><DeleteReplication><Status>Disabled</Status></DeleteReplication><Prefix>key-prefix</Prefix><Destination><Bucket>arn:aws:s3:::destinationbucket</Bucket></Destination><SourceSelectionCriteria><ReplicaModifications><Status>Enabled</Status></ReplicaModifications></SourceSelectionCriteria></Rule></ReplicationConfiguration>`,...

		if opts.Maintenance {
			writeResponse(w, http.StatusPreconditionFailed, nil, mimeNone)
		} else {
			writeResponse(w, http.StatusServiceUnavailable, nil, mimeNone)
		}
		return
	}
	writeResponse(w, http.StatusOK, nil, mimeNone)
}

// ClusterReadCheckHandler returns if the server is ready for requests.
func ClusterReadCheckHandler(w http.ResponseWriter, r *http.Request) {
	ctx := newContext(r, w, "ClusterReadCheckHandler")

  if (TF_GetCode(status) != TF_OK) return;
  if (!grad) return;

  status->status = g->graph.AddFunctionDef(grad->record->fdef(),
                                           grad->record->stack_traces());
  if (TF_GetCode(status) != TF_OK) return;

  tensorflow::GradientDef gdef;
  gdef.set_function_name(func->record->fdef().signature().name());
  gdef.set_gradient_func(grad->record->fdef().signature().name());

    TF_Status* out_status) const {
  absl::Status status;
  if (reader_ != nullptr) {
    status = reader_->GetTensor(name, out_tensor);
  } else {
    tensorflow::DataType dtype;
    tensorflow::TensorShape shape;
    status = v2_reader_->LookupDtypeAndShape(name, &dtype, &shape);
    if (status.ok()) {
      out_tensor->reset(new Tensor(dtype, shape));
      status = v2_reader_->Lookup(name, out_tensor->get());