"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.
	{

  func.func private @f() {
    func.call @g() : () -> ()
    func.return
  }
  func.func private @g() {
    func.call @f() : () -> ()
    func.return
  }
}

// -----
// Test stateful and stateless partitioned calls.
// CHECK-LABEL: func @f
func.func @f() {
  // CHECK: "tf.PartitionedCall"() <{config = "",  config_proto = "", executor_type = "", f = @g}> : () -> ()

	})
}

var (
	requestTotal = compbasemetrics.NewCounterVec(
		&compbasemetrics.CounterOpts{
			Name:           "apiserver_delegated_authz_request_total",
			Help:           "Number of HTTP requests partitioned by status code.",
			StabilityLevel: compbasemetrics.ALPHA,
		},
		[]string{"code"},
	)

	requestLatency = compbasemetrics.NewHistogramVec(
		&compbasemetrics.HistogramOpts{

//   Executor Dialect or TF Functional Dialect. Will convert to TF Functional.
//   is_supported_by_replicated_brige - If the graph targets the replicated
//   bridge. Set it to true for replicated/partitioned graphs. e.g. replicated
//   and single-core TPU graphs. Set this to false if the graph is not
//   replicated, e.g. CPU/GPU graphs. is_in_fallback_enabled_mode - Whether this

limitations under the License.
==============================================================================*/

// This transformation pass converts stateful and stateless partitioned calls
// with _xla_compile_device_type attribute to XLA launch ops.

#include <stack>

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_device.h"

  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) {

    Arg<TF_StrTensor, [{shape {N}.  The slice specs of the tensors to be restored.
Empty strings indicate that they are non-partitioned tensors.}]>:$shape_and_slices,
    // VarHandles for restored tensors.
    Variadic<TF_Tensor>:$var_handles,
    // Data types for restored tensor.
    TypeArrayAttr: $restored_dtypes,

  // Operation along with its respective operands and result Values(s).
  // The semantic of `Operation::clone` is copying given entity *into* this
  // entity. The new FuncOp body is populated by cloning partitioned ops into
  // it. Cloning Operation(s) will create cloned Value(s) for the results of a
  // cloned op, but it needs a reference to the new operand Value(s) which are

  // 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) {