telemetry.Inc(fmt.Sprintf("go/platform/host/%s/version:%s-%s", runtime.GOOS, major, minor))
}

func majorMinor(v string) (string, string, bool) {
	firstDot := strings.Index(v, ".")
	if firstDot < 0 {
		return "", "", false
	}
	major := v[:firstDot]
	v = v[firstDot+len("."):]
	endMinor := strings.IndexAny(v, ".-_")
	if endMinor < 0 {
		endMinor = len(v)
	}
	minor := v[:endMinor]
	return major, minor, true

  auto first_dot = concat.getVal()[0].getDefiningOp<mhlo::DotGeneralOp>();
  if (!first_dot)
    return rewriter.notifyMatchFailure(concat, "Operand is not dot_general");
  if (!first_dot.getLhs().getType().hasStaticShape())
    return rewriter.notifyMatchFailure(
        first_dot, "All dot_general LHS must be statically shaped");
  if (!first_dot->hasOneUse())

	s.updateLock.Lock()
	defer s.updateLock.Unlock()

	seenBefore := s.sourcesSeen.Has(source)
	adds, updates, deletes, removes, reconciles := s.merge(source, change)
	firstSet := !seenBefore && s.sourcesSeen.Has(source)

	// deliver update notifications
	switch s.mode {
	case PodConfigNotificationIncremental:
		if len(removes.Pods) > 0 {
			s.updates <- *removes
		}

      XlaComputation& computation) {
    SourceMgrDiagnosticHandler sourceMgrHandler(source_manager_, &context_);

    mlir::Operation& first_op = GetFirstOpFromMain();

    Tf2XlaRewriterTestPeer test_peer(&first_op);
    return test_peer.ImportXlaComputationIntoModule(computation);
  }

 protected:
  MLIRContext context_;
  OwningOpRef<ModuleOp> module_;

// constraint on axis and depth.
multiclass L2NormalizePatterns<Op FirstOp, Op SecondOp> {
  // This pattern constructs L2NormalizationOp from
  // Mul->Rsqrt->Sum->Square Or
  // Div->sqrt->Sum->Square
  def L2NormalizePattern1#FirstOp#SecondOp : Pat<
                  (FirstOp $x,
                     (SecondOp
                        (TFL_SumOp

      }
    }

    // Assume all the replicas have the same structure.
    mlir::TF::TPUPartitionedOutputV2Op first_op = *(ops.begin());
    mlir::ArrayAttr dims = first_op.getPartitionDimsAttr();
    StringAttr sharding = first_op.get_XlaShardingAttr();
    Operation::result_type_range output_types = first_op.getResultTypes();
    result_op = builder.create<mlir::TF::TPUPartitionedOutputV2Op>(

        List<T> result = new ArrayList<T>();
        Iterator<? extends Collection<T>> iterator = availableValuesByDescriptor.iterator();
        if (iterator.hasNext()) {
            Collection<T> firstSet = iterator.next();
            result.addAll(firstSet);
            while (iterator.hasNext()) {
                Collection<T> next = iterator.next();
                result.retainAll(next);
            }
        }
        return result;

				i0 := dst0.PixOffset(r.Min.X, r.Min.Y)
				i1 := i0 + r.Dx()
				for i := i0; i < i1; i++ {
					dst0.Pix[i] = colorIndex
				}
				firstRow := dst0.Pix[i0:i1]
				for y := r.Min.Y + 1; y < r.Max.Y; y++ {
					i0 += dst0.Stride
					i1 += dst0.Stride
					copy(dst0.Pix[i0:i1], firstRow)
				}
				return
			} else if !processBackward(dst, r, src, sp) {
				drawPaletted(dst0, r, src, sp, false)
				return
			}

        List<T> result = new ArrayList<T>();
        Iterator<? extends Collection<T>> iterator = availableValuesByDescriptor.iterator();
        if (iterator.hasNext()) {
            Collection<T> firstSet = iterator.next();
            result.addAll(firstSet);
            while (iterator.hasNext()) {
                Collection<T> next = iterator.next();
                result.retainAll(next);
            }
        }
        return result;

		return net.Dial("tcp", ts.Listener.Addr().String())
	}
	_, err := c.Get("http://firsthost.fake/")
	if err != nil {
		t.Fatal(err)
	}
	_, err = c.Get("http://firsthost.fake/nosetcookie")
	if err != nil {
		t.Fatal(err)
	}
	got := jar.log.String()
	want := `Cookies("http://firsthost.fake/")
SetCookie("http://firsthost.fake/", [name=val])
Cookies("http://secondhost.fake/secondpath")