Angenommen, Sie möchten auf die IP-Adresse/den Host des Clients in Ihrer *Pfadoperation-Funktion* zugreifen.

Dazu müssen Sie direkt auf den Request zugreifen.

```Python hl_lines="1  7-8"
{!../../docs_src/using_request_directly/tutorial001.py!}
```

Durch die Deklaration eines *Pfadoperation-Funktionsparameters*, dessen Typ der `Request` ist, weiß **FastAPI**, dass es den `Request` diesem Parameter übergeben soll.

    Thread.interrupted();
  }

  /**
   * Retrieves the result of a {@code Future} known to be done but uses the {@code get(long,
   * TimeUnit)} overload in order to test that method.
   */
  static <V> V getDoneFromTimeoutOverload(Future<V> future) throws ExecutionException {
    checkState(future.isDone(), "Future was expected to be done: %s", future);
    try {
      return getUninterruptibly(future, 0, SECONDS);

      checkNotDone();
      checkNotNull(bytes);
      mac.update(bytes);
    }

    private void checkNotDone() {
      checkState(!done, "Cannot re-use a Hasher after calling hash() on it");
    }

    @Override
    public HashCode hash() {
      checkNotDone();
      done = true;
      return HashCode.fromBytesNoCopy(mac.doFinal());
    }
  }

Wir erstellen eine `HTTPException`, die wir später an mehreren Stellen wiederverwenden (`raise`n) können.

In diese Exception fügen wir (falls vorhanden) die erforderlichen Scopes als durch Leerzeichen getrennten String ein (unter Verwendung von `scope_str`). Wir fügen diesen String mit den Scopes in den Header `WWW-Authenticate` ein (das ist Teil der Spezifikation).

= true ]; then if checkBucketExists $BUCKET ; then echo "Purging bucket '$BUCKET'." set +e ; # don't exit if this fails ${MC} rm -r --force myminio/$BUCKET set -e ; # reset `e` as active else echo "Bucket '$BUCKET' does not exist, skipping purge." fi fi # Create the bucket if it does not exist if ! checkBucketExists $BUCKET ; then echo "Creating bucket '$BUCKET'" ${MC} mb myminio/$BUCKET else echo "Bucket '$BUCKET' already exists." fi # set versioning for bucket if [ ! -z $VERSIONING ] ; then if...

= true ]; then if checkBucketExists $BUCKET ; then echo "Purging bucket '$BUCKET'." set +e ; # don't exit if this fails ${MC} rm -r --force myminio/$BUCKET set -e ; # reset `e` as active else echo "Bucket '$BUCKET' does not exist, skipping purge." fi fi # Create the bucket if it does not exist and set objectlocking if enabled (NOTE: versioning will be not changed if OBJECTLOCKING is set because it enables versioning to the Buckets created) if ! checkBucketExists $BUCKET ; then if [ ! -z $OBJECTLOCKING...

errors.invalid_kuromoji_token={0} is invalid.
errors.invalid_kuromoji_segmentation=The number of segmentations {0} does not the match number of readings {1}.
errors.invalid_str_is_included="{1}" in "{0}" is invalid.
errors.blank_password=Password is required.
errors.invalid_confirm_password=Confirm Password does not match.
errors.cannot_delete_doc_because_of_running=Crawler is running. The document cannot be deleted.

   *
   * <ul>
   *   <li>Its execution does not cause any observable side effects.
   *   <li>The computation is <i>consistent with equals</i>; that is, {@link Objects#equal
   *       Objects.equal}{@code (a, b)} implies that {@code predicate.apply(a) ==
   *       predicate.apply(b))}.
   * </ul>
   *
   * @throws NullPointerException if {@code input} is null and this predicate does not accept null
   *     arguments
   */

		Err:     "key with given key ID already exits",
	}

	// ErrKeyNotFound is an error returned by the KMS when trying to
	// use a key that does not exist.
	ErrKeyNotFound = Error{
		Code:    http.StatusNotFound,
		APICode: "kms:KeyNotFound",
		Err:     "key with given key ID does not exist",
	}

	// ErrDecrypt is an error returned by the KMS when the decryption
	// of a ciphertext failed.
	ErrDecrypt = Error{

   
   
   *TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.