// Internal errors are transient
	case nil, ErrIsEmpty, ErrIsFull, ErrAcquireLock, ErrTooMuchDataToWrite, ErrIsNotEmpty:
		return err
	default:
		r.err = err
		if r.block {
			r.readCond.Broadcast()
			r.writeCond.Broadcast()
		}
	}
	return err
}

func (r *RingBuffer) readErr(locked bool) error {
	if !locked {
		r.mu.Lock()
		defer r.mu.Unlock()
	}
	if r.err != nil {
		if r.err == io.EOF {

        // Non-parallel tensors from _EagerConst/tf.constant are implicitly
        // broadcast, i.e. set as the input to each parallel operation. This
        // allows code like "tf.constant(1.)" or "tf.reduce_sum(..., axis=1)"
        // (where the value starts on the host), without allowing other implicit
        // copies/broadcasts. Other implicit copies may be supported eventually,

```
Client #1596980209979 left the chat
```

/// tip

The app above is a minimal and simple example to demonstrate how to handle and broadcast messages to several WebSocket connections.

But keep in mind that, as everything is handled in memory, in a single list, it will only work while the process is running, and will only work with a single process.

		atomic.StoreInt64(&c.LastPong, time.Now().UnixNano())
	}
	atomic.StoreUint32((*uint32)(&c.state), uint32(s))
	if debugPrint {
		fmt.Println(c.Local, "updateState:", gotState, "->", s)
	}
	c.connChange.Broadcast()
}

// monitorState will monitor the state of the connection and close the net.Conn if it changes.
func (c *Connection) monitorState(conn net.Conn, cancel context.CancelCauseFunc) {
	c.connChange.L.Lock()

	tc, ok := ctx.Value(mcontext.ContextTraceKey).(*mcontext.TraceCtxt)
	if ok {
		netLockCtx = context.WithValue(netLockCtx, mcontext.ContextTraceKey, tc)
	}

	for index, c := range restClnts {
		wg.Add(1)
		// broadcast lock request to all nodes
		go func(index int, isReadLock bool, c NetLocker) {
			defer wg.Done()

			g := Granted{index: index}
			if c == nil {
				log("dsync: nil locker\n")
				ch <- g
				return

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*   Added colocation constraints to `StagingArea`.
*   `sparse_matmul_op` reenabled for Android builds.
*   Restrict weights rank to be the same as the broadcast target, to avoid
    ambiguity on broadcast rules.
*   Upgraded libxsmm to 1.7.1 and applied other changes for performance and
    memory usage.
*   Fixed bfloat16 integration of LIBXSMM sparse mat-mul.

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