with(it) {
                artifactName == componentId.displayName
            }
        }
        executionIdentifications.artifactName ==~ ['file1.jar', 'file2.jar']
    }

    def "transform chains are captured"() {
        settingsFile << """
            include 'producer', 'consumer'
        """

        setupBuildWithColorAttributes()
        setupExternalDependency()

        buildFile << """

	CertInfo     *CertInfo
	Store        Handle
}

type CertChainContext struct {
	Size                       uint32
	TrustStatus                CertTrustStatus
	ChainCount                 uint32
	Chains                     **CertSimpleChain
	LowerQualityChainCount     uint32
	LowerQualityChains         **CertChainContext
	HasRevocationFreshnessTime uint32
	RevocationFreshnessTime    uint32
}

import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;
import org.checkerframework.checker.nullness.qual.Nullable;

/**
 * A {@link ListenableFuture} that supports fluent chains of operations. For example:
 *
 * <pre>{@code
 * ListenableFuture<Boolean> adminIsLoggedIn =
 *     FluentFuture.from(usersDatabase.getAdminUser())
 *         .transform(User::getId, directExecutor())

import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;
import org.checkerframework.checker.nullness.qual.Nullable;

/**
 * A {@link ListenableFuture} that supports fluent chains of operations. For example:
 *
 * <pre>{@code
 * ListenableFuture<Boolean> adminIsLoggedIn =
 *     FluentFuture.from(usersDatabase.getAdminUser())
 *         .transform(User::getId, directExecutor())

    the values). The execution model for operations in this block follows the
    TensorFlow executor semantics:
      1. Operations that don’t have any transitive dependencies through the
         def/use chains may be executed in parallel
         (`tf_executor.NextIteration.Source` is the exception).
      2. SSA values in this block can be implicitly dead. This means that every

		switch node.Type() {
		case NodeField:
			node = t.newField(chain.Position(), chain.String())
		case NodeVariable:
			node = t.newVariable(chain.Position(), chain.String())
		case NodeBool, NodeString, NodeNumber, NodeNil, NodeDot:
			t.errorf("unexpected . after term %q", node.String())
		default:
			node = chain
		}
	}
	return node
}

// term:
//

		for i, val := range dst {
			di := i + index
			hh := &d.hashHead[val&hashMask]
			// Get previous value with the same hash.
			// Our chain should point to the previous value.
			d.hashPrev[di&windowMask] = *hh
			// Set the head of the hash chain to us.
			*hh = uint32(di + d.hashOffset)
		}
	}
	// Update window information.
	d.windowEnd = n
	d.index = n
}

for operations in this block follows the TensorFlow executor semantics:

1.  Operations that don’t have any transitive dependencies through the SSA
    def/use chains may be executed in parallel
    (`tf_executor.NextIteration.Source` is the exception).
2.  SSA values in this block can be implicitly dead. This means that every SSA

// when certain messages are seen.
type opensslOutputSink struct {
	handshakeComplete chan struct{}
	readKeyUpdate     chan struct{}
	all               []byte
	line              []byte
}

func newOpensslOutputSink() *opensslOutputSink {
	return &opensslOutputSink{make(chan struct{}), make(chan struct{}), nil, nil}
}

// opensslEndOfHandshake is a message that the “openssl s_server” tool will

!tf_res = tensor<!tf_type.resource<tensor<f32>>>

// Tests independent chains of two resources.

// CHECK-LABEL: func @simple_independent_chains_while_body