The new [Cookie.Partitioned] field identifies cookies with the Partitioned attribute....

				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

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,

  Option<bool> skip_resize_{
      *this, "skip-resize",
      ::llvm::cl::desc("Skip tf.ResizeBilinear and tf.ResizeNearestNeighbor")};

  Option<bool> skip_partitioned_calls_{
      *this, "skip-partitioned-calls",
      ::llvm::cl::desc(
          "Skip tf.StatefulPartitionedCall and tf.PartitionedCall")};
};

void TFToMhloPass::runOnOperation() {
  auto func = getOperation();

		"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.
	{

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

    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) {