// This filter goes through quantize and dequantize ops. Then we just
        // need to update the weight to the quantize op.
        filter.replaceAllUsesWith(new_filter_op);
      } else {
        // This filter doesn't go through quantize and dequantize ops, Then
        // we update the weight of the affine op directly.
        fc_op.setOperand(1, new_filter_op);
      }
    } else {

//
// The -u flag instructs get to update modules providing dependencies
// of packages named on the command line to use newer minor or patch
// releases when available.
//
// The -u=patch flag (not -u patch) also instructs get to update dependencies,
// but changes the default to select patch releases.
//
// When the -t and -u flags are used together, get will update
// test dependencies as well.
//

                                            << initial_element_shape);
  // Verifies if the new element type has static shape and matches the potential
  // type passed from caller. Updates the potential_element_type, if not defined
  // yet.
  auto verify_and_update_potential_element_type =
      [&](RankedTensorType new_element_type) -> bool {
    DCOMMENT("\t\tConsidering " << new_element_type << " with old "

    /** The key of the message: Display Order */
    public static final String LABELS_SORT_ORDER = "{labels.sortOrder}";

    /** The key of the message: Updated by */
    public static final String LABELS_UPDATED_BY = "{labels.updatedBy}";

    /** The key of the message: Updated Time */
    public static final String LABELS_UPDATED_TIME = "{labels.updatedTime}";

    /** The key of the message: URLs */

    status->status = tensorflow::errors::OutOfRange(
        "Cannot update edge. Input index [", dst.index,
        "] is greater than the number of total inputs [", ic_dst->num_inputs(),
        "].");
    return;
  }
  if (!ic_dst->MergeInput(dst.index, shape)) {
    status->status = tensorflow::errors::InvalidArgument(
        "Cannot update edge, incompatible shapes: ", ic_dst->DebugString(shape),

// should be performed by other components (like the volume manager). The duration
// of this call is the minimum latency for static pods to be restarted if they
// are updated with a fixed UID (most should use a dynamic UID), and no config
// updates are delivered to the pod workers while this method is running.
func (kl *Kubelet) HandlePodCleanups(ctx context.Context) error {

	}{

		{
			name:    "Immutable after update node addresses single-stack",
			nodeIPs: []string{"10.0.0.1"},
			nodeAddresses: []v1.NodeAddress{
				{Type: v1.NodeInternalIP, Address: "1.1.1.1"},
			},
			expectedPodIPs: []v1.PodIP{
				{IP: "10.0.0.1"},
			},
		},
		{
			name:    "Immutable after update node addresses dual-stack - primary address",

mlir::LogicalResult ScatterNdOp::verify() {
  ScatterNdOp op = *this;
  auto indices = op.getIndices();
  auto updates = op.getUpdates();
  auto shape = op.getShape();
  auto output = op.getOutput();

  auto updates_type = updates.getType().cast<ShapedType>();
  auto indices_type = indices.getType().cast<ShapedType>();

}

def TPUUpdateEmbeddingEnqueueOpInputsPass : Pass<"tf-tpu-update-embedding-enqueue-op-inputs", "mlir::func::FuncOp"> {
  let summary = "Updates inputs to TPU embedding enqueue ops depending on whether graph is in training mode or in evaluation mode.";

  let description = [{
    Updates inputs to TPU embedding enqueue ops depending on whether graph
    is in training mode or in evaluation mode.

	tc.Lock()
	defer tc.Unlock()

	assert.Equal(t, tc.specReplicas != tc.expectedDesiredReplicas, tc.scaleUpdated, "the scale should only be updated if we expected a change in replicas")
	assert.True(t, tc.statusUpdated, "the status should have been updated")
	if tc.verifyEvents {