# Requirements for NumPy 1.x
numpy ~= 1.26.0
wheel ~= 0.41.2
h5py >= 3.10.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.4.0, < 0.5.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version

Consider an application streaming a video over http/2. Perhaps the user pauses the video and the application stops reading bytes from this stream. The buffer will fill up, and flow control prevents the server from sending more data on this stream. When the user unpauses her video the buffer drains, the read is acknowledged, and the server proceeds to stream data.

#### Shared reader thread

   * values are null. This method may update {@code alternatingKeysAndValues} if there are duplicate
   * keys. If so, the return value will indicate how many entries are still valid, and will also
   * include a {@link Builder.DuplicateKey} in case duplicate keys are not allowed now or will not
   * be allowed on a later {@link Builder#buildOrThrow()} call.
   *

    const val MAX_CONCURRENT_STREAMS = 4

    /** HTTP/2: Size in bytes of the largest frame payload the sender will accept. */
    const val MAX_FRAME_SIZE = 5

    /** HTTP/2: Advisory only. Size in bytes of the largest header list the sender will accept. */
    const val MAX_HEADER_LIST_SIZE = 6

    /** Window size in bytes. */
    const val INITIAL_WINDOW_SIZE = 7

		go func(inbound chan []byte) {
			wg.Wait()
			defer xioutil.SafeClose(handlerIn)
			m.handleInbound(c, inbound, handlerIn)
		}(m.inbound)
	}
	// Fill outbound block.
	// Each token represents a message that can be sent to the client without blocking.
	// The client will refill the tokens as they confirm delivery of the messages.
	for i := 0; i < outboundCap; i++ {
		m.outBlock <- struct{}{}
	}

	// Handler goroutine.

// to allow automatic width to fill the grid
{
  local root = self,

  local gridWidth = 24,

  '#makeGrid':: d.func.new(
    |||
      `makeGrid` returns an array of `panels` organized in a grid with equal width
      and `panelHeight`. Row panels are used as "linebreaks", if a Row panel is collapsed,
      then all panels below it will be folded into the row.

 * acquire(1000)} will result in exactly the same throttling, if any), but it affects the throttling
 * of the <i>next</i> request. I.e., if an expensive task arrives at an idle RateLimiter, it will be
 * granted immediately, but it is the <i>next</i> request that will experience extra throttling,
 * thus paying for the cost of the expensive task.
 *

                                                TF_Status* status);

// This function will block till the operation that produces `h` has
// completed. This is only valid on local TFE_TensorHandles. The pointer
// returned will be on the device in which the TFE_TensorHandle resides (so e.g.
// for a GPU tensor this will return a pointer to GPU memory). The pointer is

        By asking questions in a structured way (following this) it will be much easier to help you.

        And there's a high chance that you will find the solution along the way and you won't even have to submit it and wait for an answer. 😎

  @Override
  public void clear() {
    Arrays.fill(keys, 0, size, null);
    Arrays.fill(values, 0, size, null);
    Arrays.fill(hashTableKToV, ABSENT);
    Arrays.fill(hashTableVToK, ABSENT);
    Arrays.fill(nextInBucketKToV, 0, size, ABSENT);
    Arrays.fill(nextInBucketVToK, 0, size, ABSENT);
    Arrays.fill(prevInInsertionOrder, 0, size, ABSENT);
    Arrays.fill(nextInInsertionOrder, 0, size, ABSENT);
    size = 0;