okforeq[types.TMAP] = true    // nil only; refined in typecheck
	okforeq[types.TFUNC] = true   // nil only; refined in typecheck
	okforeq[types.TSLICE] = true  // nil only; refined in typecheck
	okforeq[types.TARRAY] = true  // only if element type is comparable; refined in typecheck
	okforeq[types.TSTRUCT] = true // only if all struct fields are comparable; refined in typecheck

	types.IsOrdered[types.TSTRING] = true

  // type.
  if (!updated.hasRank()) return false;

  // If the original type doesn't have a rank, then refine as the updated type
  // has a rank.
  if (!original.hasRank()) return true;

  // Both types must have the same rank.
  if (original.getRank() != updated.getRank()) return false;

  // Refine if the updated type is bounded.
  return IsBounded(updated);

namespace TF {

// Returns whether type can be further refined.
bool CanBeRefined(Type type);

// Returns a new arg type based on the shape and element type. If there are
// dynamic bounds attribute to the arg, update the bounds based on the shape
// as well.
Type GetNewArgType(Type old_arg_type, ArrayRef<int64_t> shape,
                   Type element_type, mlir::MLIRContext* context);

- The [x/telemetry/config](https://pkg.go.dev/pkg/golang.org/x/telemetry/config)
  package defines the subset of telemetry data that has been approved for
  uploading by the telemetry proposal process.
- The [x/telemetry/godev](https://pkg.go.dev/pkg/golang.org/x/telemetry/godev) directory defines
  the services running at [telemetry.go.dev](https://telemetry.go.dev).

## Contributing

// Tests function with argument has no resource subtype but caller operand has a
// resource subtype, and after shape inference, function argument is refined and
// no `tf.Cast` ops are generated.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 384 : i32}} {
  // CHECK-LABEL: func @main
  func.func @main() -> tensor<f32> {

     * @return a mutable plugin dependency specification that can be used to further refine the dependency
     */
    PluginDependencySpec id(String id);

    /**
     * Adds a plugin dependency using a notation coming from a version catalog.
     * The resulting dependency spec can be refined with a version overriding
     * what the version catalog provides.
     * @param notation the plugin reference

    /**
     * Assumes that this source set will contain Java sources and therefore creates a Java
     * compile task which configuration can be refined using the provided action configuration.
     * Implicitly calls {@link #includeInAllJava()}
     * @param compilerConfiguration the configuration of the compile task
     */

using tensorflow::shape_inference::InferenceContext;
using tensorflow::shape_inference::ShapeHandle;

namespace mlir {
namespace TF {
namespace {

// Compute a refined type between two types `lhs` and `rhs`, the result type
// is always more refined (i.e. has more static information) than `lhs`
// This method will actually merge the information contained in the
// types, it is capable of refining:

// pvcResources are the set of static resources managed by quota associated with pvcs.
// for each resource in this list, it may be refined dynamically based on storage class.
var pvcResources = []corev1.ResourceName{
	corev1.ResourcePersistentVolumeClaims,
	corev1.ResourceRequestsStorage,
}

     * defined as:
     *
     * <pre>{@code
     * ((element == null) ? 0 : element.hashCode()) ^ count
     * }</pre>
     */
    @Override
    int hashCode();

    /**
     * Returns the canonical string representation of this entry, defined as follows. If the count