err = msgp.WrapError(err, "StoppedAt")
				return
			}
		case "id":
			z.ID, err = dc.ReadString()
			if err != nil {
				err = msgp.WrapError(err, "ID")
				return
			}
		case "pf":
			z.PercentFreeGoal, err = dc.ReadFloat64()
			if err != nil {
				err = msgp.WrapError(err, "PercentFreeGoal")
				return
			}
		case "rss":
			var zb0002 uint32
			zb0002, err = dc.ReadArrayHeader()

		}
		tx = tx.callbacks.Create().Execute(tx.Set("gorm:update_track_time", true))
	case reflect.Struct:
		if err := tx.Statement.Parse(value); err == nil && tx.Statement.Schema != nil {
			for _, pf := range tx.Statement.Schema.PrimaryFields {
				if _, isZero := pf.ValueOf(tx.Statement.Context, reflectValue); isZero {
					return tx.callbacks.Create().Execute(tx)
				}
			}
		}

		fallthrough
	default:

	instructions["JP"] = x86.AJPS   /* alternate */
	instructions["JPC"] = x86.AJPC  /* parity clear (PF = 0) */
	instructions["JPE"] = x86.AJPS  /* alternate */
	instructions["JPL"] = x86.AJPL  /* non-negative (plus) (SF = 0) */
	instructions["JPO"] = x86.AJPC  /* alternate */
	instructions["JPS"] = x86.AJPS  /* parity set (PF = 1) */
	instructions["JS"] = x86.AJMI   /* alternate */
	instructions["JZ"] = x86.AJEQ   /* alternate */

	StoppedAt       time.Time         `msg:"stopTs"` // Time when rebalance-stop was issued.
	ID              string            `msg:"id"`     // ID of the ongoing rebalance operation
	PercentFreeGoal float64           `msg:"pf"`     // Computed from total free space and capacity at the start of rebalance
	PoolStats       []*rebalanceStats `msg:"rss"`    // Per-pool rebalance stats keyed by pool index
}

If <code>x</code> is <code>nil</code>, an attempt to evaluate <code>*x</code>
will cause a <a href="#Run_time_panics">run-time panic</a>.
</p>

<pre>
&amp;x
&amp;a[f(2)]
&amp;Point{2, 3}
*p
*pf(x)

var x *int = nil
*x   // causes a run-time panic
&amp;*x  // causes a run-time panic
</pre>


<h3 id="Receive_operator">Receive operator</h3>

<p>

*   TensorFlow Debugger (tfdbg):
    *   Display summaries of numeric tensor values with the `-s` flag to command
        `print_tensor` or `pt`.
    *   Display feed values with the `print_feed` or `pf` command and clickable
        links in the curses UI.
    *   Runtime profiler at the op level and the Python source line level with
        the `run -p` command.