// since it is part of the ComponentArtifactIdentifier returned by the transform.
            // For absolute paths, the name is already part of the normalized path,
            // and for all the other normalization strategies we use the name directly.
            transform.getInputArtifactNormalizer() == InputNormalizer.ABSOLUTE_PATH
                ? inputArtifact.getAbsolutePath()

	for parentUser, info := range parentUsers {
		if !sys.LDAPConfig.IsLDAPUserDN(parentUser) {
			continue
		}

		if info.subClaimValue != "" {
			// we need to ask LDAP about the actual user DN not normalized DN.
			allDistNames = append(allDistNames, info.subClaimValue)
		} else {
			allDistNames = append(allDistNames, parentUser)
		}
	}

        }

        private void assertHasFailure(Matcher<? super String> matcher, Consumer<? super Failure> action) {
            Matcher<String> normalized = normalizedLineSeparators(matcher);
            for (FailureDetails failure : failures) {
                if (normalized.matches(failure.description)) {
                    action.accept(failure);
                    return;
                }
            }

				suite.TestLDAPCyrillicUser(c)
				suite.TearDownSuite(c)
			},
		)
	}
}

// This test is for a fix added to handle non-normalized base DN values in the
// LDAP configuration. It runs the existing LDAP sub-tests with a non-normalized
// LDAP configuration.
func TestIAMWithLDAPNonNormalizedBaseDNConfigServerSuite(t *testing.T) {
	for i, testCase := range iamTestSuites {
		t.Run(

	subClaim = "sub"
	audClaim = "aud"
	issClaim = "iss"

	// JWT claim to check the parent user
	parentClaim = "parent"

	// LDAP claim keys
	ldapUser       = "ldapUser"       // this is a key name for a normalized DN value
	ldapActualUser = "ldapActualUser" // this is a key name for the actual DN value
	ldapUserN      = "ldapUsername"   // this is a key name for the short/login username
	// Claim key-prefix for LDAP attributes

  }];

  let arguments = (ins
    Arg<TF_Float32Tensor, [{A 4D Tensor for input data.}]>:$x,
    Arg<TF_Float32Tensor, [{A 1D Tensor for scaling factor, to scale the normalized x.}]>:$scale,
    Arg<TF_Float32Tensor, [{A 1D Tensor for offset, to shift to the normalized x.}]>:$offset,
    Arg<TF_Float32Tensor, [{A 1D Tensor for population mean. Used for inference only;
must be empty for training.}]>:$mean,

utilization under steady load.\n\n- **vCPU / 1k rps:** shows vCPU utilization by the main Istio components normalized by 1000 requests/second. When idle or low traffic, this chart will be blank. The curve for istio-proxy refers to the services sidecars only.\n- **vCPU:** vCPU utilization by Istio components, not normalized.\n- **Memory:** memory footprint for the components. Telemetry and policy are normalized by 1k rps, and no data is shown  when there is no traffic. For ingress and istio-proxy, the data...

	input := &example.Pod{ObjectMeta: metav1.ObjectMeta{Name: "foo", Namespace: "test-ns"}}
	key := computePodKey(input)

	// serialize input into etcd with data that would be normalized by a write -
	// in this case, leading whitespace
	revertTransformer := store.UpdatePrefixTransformer(
		func(transformer *PrefixTransformer) value.Transformer {

    // Compute the total number of integer steps and convert to the HLO
    // dimension tensor.
    auto normalized =
        rewriter.create<mhlo::DivOp>(op.getLoc(), abs_cast, delta_cast);
    auto ceil = rewriter.create<mhlo::CeilOp>(op.getLoc(), normalized);
    auto steps = rewriter.create<mhlo::ConvertOp>(
        op.getLoc(),

        for details on how to use the callback.
*   `tf.image.extract_glimpse` has been updated to correctly process the case
    where `centered=False` and `normalized=False`. This is a breaking change as
    the output is different from (incorrect) previous versions. Note this
    breaking change only impacts `tf.image.extract_glimpse` and