if err != nil {
					c.Expires = time.Time{}
					break
				}
			}
			c.Expires = exptime.UTC()
			continue
		case "path":
			c.Path = val
			continue
		case "partitioned":
			c.Partitioned = true
			continue
		}
		c.Unparsed = append(c.Unparsed, parts[i])
	}
	return c, nil
}

// readSetCookies parses all "Set-Cookie" values from

}
}  // namespace

// Rewrites unpartitioned resource reads and writes to partitioned resource
// reads and writes. The TPU computation from the frontend is generated in such
// a way that resource operations operate on the unpartitioned resource handle
// (from a `tf.TPUReplicatedInput`). This results in resource reads and writes
// on the unpartitioned resource handle post resource op decomposition/lifting.

using PartitionedClusterOutputMap = absl::flat_hash_map<
    uint64_t, llvm::SmallVector<mlir::TF::TPUPartitionedOutputV2Op, 8>>;

// Returns the partitioned output ops from the cluster if there are any,
// along with any single user identity ops between them. Not all outputs
// of a cluster must be partitioned, so the output is a map from cluster
// output ids to ops.
std::tuple<PartitionedClusterOutputMap,

		"cookie-15=samesite-none; SameSite=None",
	},
	{
		&Cookie{Name: "cookie-16", Value: "partitioned", SameSite: SameSiteNoneMode, Secure: true, Path: "/", Partitioned: true},
		"cookie-16=partitioned; Path=/; Secure; SameSite=None; Partitioned",
	},
	// The "special" cookies have values containing commas or spaces which
	// are disallowed by RFC 6265 but are common in the wild.
	{

	const maxInsertion = 12

	var (
		wasBalanced    = true // whether the last partitioning was reasonably balanced
		wasPartitioned = true // whether the slice was already partitioned
	)

	for {
		length := b - a

		if length <= maxInsertion {
			insertionSortCmpFunc(data, a, b, cmp)
			return
		}

		// Fall back to heapsort if too many bad choices were made.
		if limit == 0 {

	const maxInsertion = 12

	var (
		wasBalanced    = true // whether the last partitioning was reasonably balanced
		wasPartitioned = true // whether the slice was already partitioned
	)

	for {
		length := b - a

		if length <= maxInsertion {
			insertionSortOrdered(data, a, b)
			return
		}

		// Fall back to heapsort if too many bad choices were made.
		if limit == 0 {

  added_func_op.setName(builder.getStringAttr(function_name));
  added_call_op.setCallee(builder.getStringAttr(function_name));
}

// Raises partitioned sequential `Operations` from a block to a new function
// definition. `Operations` are partitioned into classes from the cartesian
// product of possible devices and inference datatypes. For example, we might

			pdqsort{{.FuncSuffix}}(data, mid+1, b, limit {{.ExtraArg}})
			b = mid
		}
	}
}

// partition{{.FuncSuffix}} does one quicksort partition.
// Let p = data[pivot]
// Moves elements in data[a:b] around, so that data[i]<p and data[j]>=p for i<newpivot and j>newpivot.
// On return, data[newpivot] = p
func partition{{.FuncSuffix}}{{.TypeParam}}(data {{.DataType}}, a, b, pivot int {{.ExtraParam}}) (newpivot int, alreadyPartitioned bool) {

  // Add TF->TF lowering patterns.
  TF::PopulateTFLoweringBeforeHLOPatterns(context, &legalize_lower_patterns);

  if (tf2xla_fallback_device_type && prefer_tf2xla) {
    VLOG(1) << "TF to XLA legalization patterns are partitioned by op into "
               "either native MLIR legalization, or TF2XLA fallback "
               "legalzation, with a preference toward TF2XLA.";
  } else if (tf2xla_fallback_device_type) {

      FunctionType::get(func.getContext(), new_input_types,
                        func.front().getTerminator()->getOperandTypes()));
}

// Contains cached information for decomposed callee functions for (stateful)
// partitioned call ops.
struct PartitionedCallStackOpsInfo {
  bool signature_change;
  func::FuncOp decomposed_callee;
  llvm::SmallDenseMap<int64_t, int64_t> stack_var_arg_to_size_arg;
};