async function getDataBatch(page) {
    const response = await fetch(`https://api.github.com/search/repositories?q=topic:fastapi&per_page=100&page=${page}`, { headers: { Accept: 'application/vnd.github.mercy-preview+json' } })
    const data = await response.json()
    return data
}

async function getData() {
    let page = 1
    let data = []
    let dataBatch = await getDataBatch(page)

   * interned, and so does not prevent these instances from being garbage-collected. This most
   * likely does not perform as well as {@link #newStrongInterner}, but is the best alternative when
   * the memory usage of that implementation is unacceptable.
   */
  @GwtIncompatible("java.lang.ref.WeakReference")
  public static <E> Interner<E> newWeakInterner() {
    return newBuilder().weak().build();
  }

limitations under the License.
==============================================================================*/
#include "tensorflow/c/eager/gradients.h"

#include <memory>

#include "absl/container/flat_hash_set.h"
#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"

  public
  static HashCode hash(File file, HashFunction hashFunction) throws IOException {
    return asByteSource(file).hash(hashFunction);
  }

  /**
   * Fully maps a file read-only in to memory as per {@link
   * FileChannel#map(java.nio.channels.FileChannel.MapMode, long, long)}.
   *
   * <p>Files are mapped from offset 0 to its length.
   *
   * <p>This only works for files ≤ {@link Integer#MAX_VALUE} bytes.

					}
				} else {
					stopFn(result, nil)
				}
				ri.trackCurrentBucketObject(r.Bucket, result, success, attempts)
				globalBatchJobsMetrics.save(job.ID, ri)
				// persist in-memory state to disk after every 10secs.
				batchLogIf(ctx, ri.updateAfter(ctx, api, 10*time.Second, job))
				if success {
					break
				}
				if delay > 0 {

limitations under the License.
==============================================================================*/

#include "tensorflow/c/eager/parallel_device/parallel_device.h"

#include <cstring>
#include <memory>

#include "absl/status/status.h"
#include "absl/strings/str_cat.h"
#include "absl/types/optional.h"
#include "absl/types/variant.h"
#include "tensorflow/c/eager/c_api.h"

        "The data was too large to fit into the specified buffer.", "A device attached to the system is not functioning.", "Incorrect function.",
        "The parameter is incorrect.", "Invalid access to memory location.", "The handle is invalid.", "The parameter is incorrect.",
        "The system cannot find the file specified.", "The system cannot find the file specified.", "End of file", "More data is available.",

And then, right after the `yield`, we unload the model. This code will be executed **after** the application **finishes handling requests**, right before the *shutdown*. This could, for example, release resources like memory or a GPU.

/// tip

The `shutdown` would happen when you are **stopping** the application.

Maybe you need to start a new version, or you just got tired of running it. 🤷

///

### Lifespan function

fsGroupChangePolicy: "OnRootMismatch" # Additational pod annotations podAnnotations: {} # Additional pod labels podLabels: {} ## Configure resource requests and limits ## ref: http://kubernetes.io/docs/user-guide/compute-resources/ ## resources: requests: memory: 16Gi ## List of policies to be created after minio install ## ## In addition to default policies [readonly|readwrite|writeonly|consoleAdmin|diagnostics] ## you can define additional policies with custom supported actions and resources policies: []...

  static final int DEFAULT_SIZE = 3;

  /**
   * Minimum size of the hash table of a compact hash-based collection. Because small hash tables
   * use a byte[], any smaller size uses the same amount of memory due to object padding.
   */
  private static final int MIN_HASH_TABLE_SIZE = 4;

  private static final int BYTE_MAX_SIZE = 1 << Byte.SIZE; // 2^8 = 256