// ip rule add fwmark 0x111/0xfff pref 32764 lookup 100
		//
		// Adds in-pod rules for marking packets with the istio-specific TPROXY mark.
		// A very similar mechanism is used for sidecar TPROXY.
		//
		// TODO largely identical/copied from tools/istio-iptables/pkg/capture/run_linux.go
		inpodMarkRule := netlink.NewRule()
		inpodMarkRule.Family = family
		inpodMarkRule.Table = RouteTableInbound
		inpodMarkRule.Mark = InpodTProxyMark

	    the same array length.</li>

	<li>Two slice types are identical if they have identical element types.</li>

	<li>Two struct types are identical if they have the same sequence of fields,
	    and if corresponding fields have the same names, and identical types,
	    and identical tags.
	    <a href="#Exported_identifiers">Non-exported</a> field names from different

    assertEquals(
        "a.b", new ClassInfo(FILE, "a/b/Foo.class", getClass().getClassLoader()).getPackageName());
  }

  // Test that ResourceInfo.urls() returns identical content to ClassLoader.getResources()


  @AndroidIncompatible
  public void testGetClassPathUrls() throws Exception {
    if (isWindows()) {

   * set of the empty set is not the empty set, but a one-element set containing the empty set.
   *
   * <p>The returned set and its constituent sets use {@code equals} to decide whether two elements
   * are identical, even if the input set uses a different concept of equivalence.
   *
   * <p><i>Performance notes:</i> while the power set of a set with size {@code n} is of size {@code

		}
		// Make sure to return object info to provide extra information.
		return toObjectErr(errMethodNotAllowed, bucket, object)
	}
	// verify that the object queued for transition is identical to that on disk.
	if !opts.MTime.Equal(fi.ModTime) || !strings.EqualFold(opts.Transition.ETag, extractETag(fi.Metadata)) {
		return toObjectErr(errFileNotFound, bucket, object)
	}

    * Whether you represent a ragged tensor as a `tf.RaggedTensor` or using [keras masking](https://www.tensorflow.org/guide/keras/masking_and_padding), the returned loss values should be the identical to each other. In previous versions Keras may have silently ignored the mask.
 * If you use masked losses with Keras the loss values may be different in TensorFlow `2.12` compared to previous versions.