for (Region& region : op->getRegions()) {
        for (auto region_arg : TF::filter_resources(region.getArguments()))
          assign_id(region_arg);
      }

      // Assign ID for all results.
      for (auto result : TF::filter_resources(op->getResults()))
        assign_id(result);
    });

    // Now walk each operation, and annotate it wil remarks for aliases for
    // each resource type result

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-prune-unused-nodes -tf-control-output-arrays=AssignAdd -o - | FileCheck --check-prefix=PRUNE %s
# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-prune-unused-nodes -tf-control-output-arrays=Variable/Assign,AssignAdd -o - | FileCheck --check-prefix=PRESERVE %s

# ```
# import tensorflow as tf
#
# with tf.compat.v1.Graph().as_default() as g:
#   w = tf.constant(2.0)
#   x = tf.constant(3.0)
#   y = tf.constant(4.0)
#   var = tf.Variable(2.0)
#   var_add = var.assign_add(1.0)
#   with g.control_dependencies([var_add]):
#     z0, z1, z2 = tf.identity_n((w, x, y))
#
#   a = tf.add(z1, z2)
# ```

node {
  name: "w"
  op: "Const"
  attr {
    key: "dtype"
    value {

			Type:   api.PodScheduled,
			Status: api.ConditionTrue,
		})
		finalPod = pod
		return pod, nil
	}), dryRun, nil)
	return finalPod, err
}

// assignPod assigns the given pod to the given machine.
func (r *BindingREST) assignPod(ctx context.Context, podUID types.UID, podResourceVersion, podID string, machine string, annotations map[string]string, dryRun bool) (err error) {

     * <p>
     * If no credentials have been assigned to this repository, an empty set of credentials of the specified type is assigned to this repository and returned.
     *
     * @param credentialsType type of the credential
     * @return The credentials
     * @throws IllegalArgumentException when the credentials assigned to this repository are not assignable to the specified type
     */

  ImmediateOpPtr assign_op(ctx->CreateOperation());
  TF_RETURN_IF_ERROR(assign_op->Reset("AssignVariableOp", nullptr));
  TF_RETURN_IF_ERROR(assign_op->SetAttrType("dtype", dtype));
  TF_RETURN_IF_ERROR(assign_op->AddInput(variable_handle));
  TF_RETURN_IF_ERROR(assign_op->AddInput(value));

  int num_retvals = 0;
  TF_RETURN_IF_ERROR(assign_op->Execute({}, &num_retvals));
  return Status();
}

  // Inherit the constructors.
  using OpRewritePattern<TF::AssignVariableOp>::OpRewritePattern;

  LogicalResult match(TF::AssignVariableOp assign_op) const override {
    Value resource_operand = assign_op.getOperand(0);
    Value assigned_value_operand = assign_op.getOperand(1);

    if (isa<TF::VarHandleOp>(resource_operand.getDefiningOp()) &&
        isa<TF::ConstOp>(assigned_value_operand.getDefiningOp())) {

package v1

// NodeConfigStatusApplyConfiguration represents an declarative configuration of the NodeConfigStatus type for use
// with apply.
type NodeConfigStatusApplyConfiguration struct {
	Assigned      *NodeConfigSourceApplyConfiguration `json:"assigned,omitempty"`
	Active        *NodeConfigSourceApplyConfiguration `json:"active,omitempty"`
	LastKnownGood *NodeConfigSourceApplyConfiguration `json:"lastKnownGood,omitempty"`

                val assigned = objectReflection.properties.getValue(property)
                applyPropertyValue(objectReflection.objectOrigin, property, assigned)
                apply(assigned.value, conversionFilter)
                // TODO: record properties assigned in function calls or constructors, so that
                //       we can check that all properties were assigned
            }

type CPUsProvider interface {
	// GetCPUs returns information about the cpus assigned to pods and containers
	GetCPUs(podUID, containerName string) []int64
	// GetAllocatableCPUs returns the allocatable (not allocated) CPUs
	GetAllocatableCPUs() []int64
}

type MemoryProvider interface {
	// GetMemory returns information about the memory assigned to containers
	GetMemory(podUID, containerName string) []*podresourcesapi.ContainerMemory