// from PyTorch
// %0 = compare{GT}(%lhs_value, %rhs_value)
// %1 = select(%0, %lhs_value, %rhs_value)
// %2 = compare{EQ}(%lhs_value, %rhs_value)
// %3 = minimum(%lhs_index, %rhs_index)
// %4 = select(%0, %lhs_index, %rhs_index)
// %5 = select(%2, %3, %4)
// return %1, %5
LogicalResult MatchReduceToArgMinMaxType2(mhlo::ReduceOp reduce_op,

                select(CommonBuilds.PROJECT_PATH_TEST, CommonBuilds.BUILD_PATH_COMPILE),
                Collections.singletonList(system),
                nonTransitiveDependencyScopes));
        result.add(internalScopeManager.createResolutionScope(
                RS_TEST_RUNTIME,
                InternalScopeManager.Mode.ELIMINATE,

    hdrs = ["posix_filesystem_helper.h"],
    deps = [":copy_file"],
)

# On Linux, we can copy files faster using `sendfile`. But not elsewhere.
# Hence, this private library to select which implementation to use.
cc_library(
    name = "copy_file",
    srcs = select({
        "//tensorflow:linux_x86_64": ["copy_file_linux.cc"],
        "//conditions:default": ["copy_file_portable.cc"],
    }),
    hdrs = ["copy_file.h"],

        this.artifactSetResolver = artifactSetResolver;
        this.artifactVariantSelector = artifactVariantSelector;
    }

    @Override
    public SelectedArtifactSet select(ArtifactSelectionSpec spec) {
        SelectedArtifactResults artifacts = artifactsResults.select(artifactVariantSelector, spec, false);
        return new DefaultSelectedArtifactSet(artifactSetResolver, graphResults, artifacts.getArtifacts(), resolutionHost);
    }

	q.x.Set(x3)
	q.y.Set(y3)
	q.z.Set(z3)
	return q
}

// Select sets q to p1 if cond == 1, and to p2 if cond == 0.
func (q *{{.P}}Point) Select(p1, p2 *{{.P}}Point, cond int) *{{.P}}Point {
	q.x.Select(p1.x, p2.x, cond)
	q.y.Select(p1.y, p2.y, cond)
	q.z.Select(p1.z, p2.z, cond)
	return q
}

// A {{.p}}Table holds the first 15 multiples of a point at offset -1, so [1]P

			resp, err := con.upstreamDeltas.Recv()
			if err != nil {
				upstreamErr(con, err)
				return
			}
			select {
			case con.deltaResponsesChan <- resp:
			case <-con.stopChan:
			}
		}
	}()

	go p.handleUpstreamDeltaRequest(con)
	go p.handleUpstreamDeltaResponse(con)

	for {
		select {
		case err := <-con.upstreamError:
			return err
		case err := <-con.downstreamError:

by a different role than the prod version.

The intended users of this repo are users running Istio in production who want to select, tune
and understand each binary that gets deployed, and select which combination to use.

Note: each component can be installed in parallel with an existing Istio 1.0 or 1.1 installation in

log4j:log4j:1.2.16 FAILED
   Failures:
      - Could not resolve log4j:log4j:1.2.16.
          - Module 'log4j:log4j' has been rejected:
               Cannot select module with conflict on capability 'log4j:log4j:1.2.16' also provided by [org.slf4j:log4j-over-slf4j:1.7.10(compile)]

log4j:log4j:1.2.16 FAILED
\--- org.apache.zookeeper:zookeeper:3.4.9
     \--- compileClasspath

org.slf4j:log4j-over-slf4j:1.7.10 FAILED
   Failures:

    %6 = mhlo.compare  GE, %arg1, %arg3 : (tensor<i32>, tensor<i32>) -> tensor<i1>
    %7 = mhlo.select %6, %arg1, %arg3 : tensor<i1>, tensor<i32>
    %8 = mhlo.compare  EQ, %arg1, %arg3 : (tensor<i32>, tensor<i32>) -> tensor<i1>
    %9 = mhlo.minimum %arg2, %arg4 : tensor<i32>
    %10 = mhlo.select %6, %arg2, %arg4 : tensor<i1>, tensor<i32>
    %11 = mhlo.select %8, %9, %10 : tensor<i1>, tensor<i32>
    mhlo.return %7, %11 : tensor<i32>, tensor<i32>
  }

                select(CommonBuilds.PROJECT_PATH_TEST, CommonBuilds.BUILD_PATH_COMPILE),
                Collections.singletonList(system),
                nonTransitiveDependencyScopes));
        result.add(internalScopeManager.createResolutionScope(
                RS_TEST_RUNTIME,
                InternalScopeManager.Mode.ELIMINATE,