}
    # This should be modeled as unit attribute
    attr {
      key: "_input_shapes"
      value {
      }
    }
  }
}
versions {
  producer: 85
  min_consumer: 12
}

# Match partitioned call in main and capture the callee name.
# CHECK-LABEL: func @main
# CHECK: tf.PartitionedCall
# CHECK-SAME: f = @[[FUNCTION:[a-zA-Z0-9_]*]]

# Verify that callee has the unit attribute tf._input_shapes.

  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.
	{

  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_authn_request_total",
			Help:           "Number of HTTP requests partitioned by status code.",
			StabilityLevel: compbasemetrics.ALPHA,
		},
		[]string{"code"},
	)

	requestLatency = compbasemetrics.NewHistogramVec(
		&compbasemetrics.HistogramOpts{

			pdqsort(data, mid+1, b, limit)
			b = mid
		}
	}
}

// partition 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(data Interface, a, b, pivot int) (newpivot int, alreadyPartitioned bool) {
	data.Swap(a, pivot)

}

// partitionEqual_func partitions data[a:b] into elements equal to data[pivot] followed by elements greater than data[pivot].
// It assumed that data[a:b] does not contain elements smaller than the data[pivot].
func partitionEqual_func(data lessSwap, a, b, pivot int) (newpivot int) {
	data.Swap(a, pivot)
	i, j := a+1, b-1 // i and j are inclusive of the elements remaining to be partitioned

	for {

	})
}

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{

	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 {

//   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