peerCertificates := make([]*x509.Certificate, 0, len(r.TLS.PeerCertificates))
	for _, cert := range r.TLS.PeerCertificates {
		if cert.IsCA {
			continue
		}
		peerCertificates = append(peerCertificates, cert)
	}
	r.TLS.PeerCertificates = peerCertificates

	// Now, we have to check that the client has provided exactly one leaf
	// certificate that we can map to a policy.

		return cache, err
	}

	cache.Info.updates = updates

	dataUsageInfo, err := scanDataFolder(ctx, disks, s.drivePath, cache, func(item scannerItem) (sizeSummary, error) {
		// Look for `xl.meta/xl.json' at the leaf.
		if !strings.HasSuffix(item.Path, SlashSeparator+xlStorageFormatFile) &&
			!strings.HasSuffix(item.Path, SlashSeparator+xlStorageFormatFileV1) {
			// if no xl.meta/xl.json found, skip the file.

  Type operand_type = op->getOperand(0).getType();
  Type result_type = result.getType();
  auto new_type = GetCastCompatibleType(operand_type, result_type);
  if (!new_type) {
    // Combine shape information when leaf element types are not the same, not
    // including shape info in subtypes.
    auto ranked_operand_type = mlir::dyn_cast<RankedTensorType>(operand_type);
    if (!ranked_operand_type) return false;

		}()
	}

	// Build list of all actions, assigning depth-first post-order priority.
	// The original implementation here was a true queue
	// (using a channel) but it had the effect of getting
	// distracted by low-level leaf actions to the detriment
	// of completing higher-level actions. The order of
	// work does not matter much to overall execution time,
	// but when running "go test std" it is nice to see each test

		for _, p := range inlMarkList {
			if p.As != obj.ANOP {
				s.pp.CurFunc.LSym.Func().AddInlMark(p, inlMarks[p])
			}
		}

		if e.stksize == 0 && !hasCall {
			// Frameless leaf function. It doesn't need any preamble,
			// so make sure its first instruction isn't from an inlined callee.
			// If it is, add a nop at the start of the function with a position
			// equal to the start of the function.

    *   Add support for bitcasting to/from uint32 and uint64.
    *   Added a subclass of Estimator that can be created from a SavedModel
        (SavedModelEstimator).
    *   Adds leaf index modes as an argument.
    *   Allow a different output shape from the input in
        tf.contrib.image.transform.
    *   Change the state_size order of the StackedRNNCell to be natural order.