// https://gradle.slack.com/archives/C14BJHC2K/p1534945935000100
// split into "migrating builds" vs. "migrating scripts"?

As we've seen above, both scripts using the Kotlin DSL and those using the Groovy DSL can participate in the same build.
In addition, Gradle plugins from the _buildSrc_ directory, an included build or an external location can be implemented using any JVM language.

	nn := typecheck.TempAt(pos, f.fe.Func(), typ) // Note: adds new auto to fn.Dcl list
	nn.SetNonMergeable(true)
	return nn
}

// IsMergeCandidate returns true if variable n could participate in
// stack slot merging. For now we're restricting the set to things to
// items larger than what CanSSA would allow (approximateky, we disallow things
// marked as open defer slots so as to avoid complicating liveness

                    edge("org:foo:1.4.1", "org:foo:1.4.9")
                }
                edge("org:foo:1.3.0", "org:foo:1.4.9")
            }
        }
    }

    void "parent pom does not participate in forcing mechanism"() {
        mavenRepo.module("org", "foo", '1.3.0').publish()
        mavenRepo.module("org", "foo", '2.4.0').publish()

        def parent = mavenRepo.module("org", "someParent", "1.0")

  - If the custom resource does not participate the spec/status convention, the metadata.generation of the CR increments when there is any change to the CR, except for changes to the metadata.

	}

	// More than one predecessor

	if updating {
		// After the first approximation, i.e., when updating, results
		// can only get smaller, because initially backedge
		// predecessors do not participate in the intersection.  This
		// means that for the update, given the prior approximation of
		// startState, there is no need to re-intersect with unchanged
		// blocks.  Therefore remove unchanged blocks from the

// In-process TensorFlow server functionality, for use in distributed training.
// A Server instance encapsulates a set of devices and a Session target that
// can participate in distributed training. A server belongs to a cluster
// (specified by a ClusterSpec), and corresponds to a particular task in a
// named job. The server can communicate with any other server in the same
// cluster.

 * file. During build initialisation, Gradle assembles a <code>Project</code> object for each project which is to
 * participate in the build, as follows:</p>
 *
 * <ul>
 *
 * <li>Create a {@link org.gradle.api.initialization.Settings} instance for the build.</li>
 *

		if want != got {
			t.Errorf("%s: got %q; want %q", segment, got, want)
		}
		delete(wantOut, segment)

		// We must explicitly assert properties of the
		// Signature's receiver since it doesn't participate
		// in Identical() or String().
		sig, _ := sel.Type().(*Signature)
		if sel.Kind() == MethodVal {
			got := sig.Recv().Type()
			want := sel.Recv()
			if !Identical(got, want) {

		if want != got {
			t.Errorf("%s: got %q; want %q", syntax, got, want)
		}
		delete(wantOut, syntax)

		// We must explicitly assert properties of the
		// Signature's receiver since it doesn't participate
		// in Identical() or String().
		sig, _ := sel.Type().(*Signature)
		if sel.Kind() == MethodVal {
			got := sig.Recv().Type()
			want := sel.Recv()
			if !Identical(got, want) {

  // BatchMatMul should have exactly the same batch dimensions and
  // BatchMatMulV2 should have broadcastable batch dimensions.
  //
  // The last two dimensions are non-batch dimensions that don't need to
  // participate in batch dimension compatibility check.
  if (std::is_same<OpT, BatchMatMulOp>()) {
    for (const auto& dim_pairs : llvm::zip(x_batches, y_batches)) {
      int64_t x_dim = std::get<0>(dim_pairs);