// Get rule after removal
        matched = ruleManager.getRule(responseData);
        assertNull(matched); // No matching rule now

        // Add rule4 which matches
        ruleManager.addRule(rule4);
        matched = ruleManager.getRule(responseData);
        assertEquals("rule4", matched.getRuleId());
    }

    /**
     * Test rule replacement scenario
     */

	if err != nil {
		return err
	}
	*ef = BatchJobExpireFilter(tmp)
	ef.line, ef.col = value.Line, value.Column
	return err
}

// Matches returns true if obj matches the filter conditions specified in ef.
func (ef BatchJobExpireFilter) Matches(obj ObjectInfo, now time.Time) bool {
	switch ef.Type {
	case BatchJobExpireObject:
		if obj.DeleteMarker {
			return false
		}
	case BatchJobExpireDeleted:

        during the python Dataset object creation instead doing it during
        Iterator creation time.
    *   Add `batch_dims` argument to `tf.gather`.
    *   The behavior of `tf.gather` is now correct when `axis=None` and
        `batch_dims<0`.
    *   Update docstring for gather to properly describe the non-empty
        `batch_dims` case.
    *   Removing of dtype in the constructor of initializers and partition_info

			msg:  "key can't be empty",
		}
	}
	return nil
}

// Empty indicates if kv is not set
func (kv BatchJobKV) Empty() bool {
	return kv.Key == "" && kv.Value == ""
}

// Match matches input kv with kv, value will be wildcard matched depending on the user input
func (kv BatchJobKV) Match(ikv BatchJobKV) bool {
	if kv.Empty() {
		return true
	}
	if strings.EqualFold(kv.Key, ikv.Key) {

        assertFalse(auth.matches("ftp://hostname:111/"));
        assertFalse(auth.matches("ftp://hostname:xx/"));
        assertFalse(auth.matches("ftp://hoge/test/aaa.html"));
        assertFalse(auth.matches("ftp://hoge/test"));
        assertFalse(auth.matches("ftp://hoge/"));
        assertFalse(auth.matches("ftp://hoge"));
        assertFalse(auth.matches("ftp://"));
        assertFalse(auth.matches("http://hostname/"));

And be compatible with all of them at the same time.

///

Create a utility function to hash a password coming from the user.

And another utility to verify if a received password matches the hash stored.

And another one to authenticate and return a user.

{* ../../docs_src/security/tutorial004_an_py310.py hl[8,49,56:57,60:61,70:76] *}

/// note

    /**
     * Creates a new SitemapsRule instance.
     */
    public SitemapsRule() {
        super();
    }

    /**
     * Matches the given response data against the sitemaps rule.
     * @param responseData The response data.
     * @return true if the response data matches the sitemaps rule, false otherwise.
     */
    @Override
    public boolean match(final ResponseData responseData) {

        """
        Search for credentials with client_grants
        """
        if self.cid is not None:
            fetcher = self._create_credentials_fetcher()
            return RefreshableCredentials.create_from_metadata(
                metadata=fetcher(),
                refresh_using=fetcher,
                method=self.METHOD,
            )
        else:
            return None

potentially exploit a vulnerability in the TensorFlow code that handles videos,
which could allow them to execute arbitrary code on the system running
TensorFlow.

It is important to keep TensorFlow up to date with the latest security patches
and follow the sandboxing guideline above to protect against these types of
vulnerabilities.

## Security properties of execution modes

TensorFlow has several execution modes, with Eager-mode being the default in v2.

	if len(newID) != len(id) {
		log.Fatalf("%s: build ID length mismatch %q vs %q", file, id, newID)
	}

	if len(matches) == 0 {
		return
	}

	f, err = os.OpenFile(file, os.O_RDWR, 0)
	if err != nil {
		log.Fatal(err)
	}
	if err := buildid.Rewrite(f, matches, newID); err != nil {
		log.Fatal(err)
	}
	if err := f.Close(); err != nil {
		log.Fatal(err)
	}