T params = Cast.uncheckedCast(spec.getIsolatedParams().isolate());
        return new SimpleActionExecutionSpec<>(spec.getImplementationClass(), params, spec.isInternalServicesRequired());
    }

    @Override
    public <T extends WorkParameters> SimpleActionExecutionSpec<T> newSimpleSpec(TransportableActionExecutionSpec spec) {

        boolean hasRules = this.modelRuleSourceDetector.hasRules(type);

        if (implementsInterface) {
            @SuppressWarnings("unchecked") Class<? extends Plugin<?>> cast = (Class<? extends Plugin<?>>) type;
            return Cast.uncheckedCast(toImperative(cast, hasRules));
        } else if (hasRules) {
            return new PotentialPureRuleSourceClassPlugin<T>(type);
        } else {

        setDidWork(result.getDidWork());
    }

    private Compiler<StaticLibraryArchiverSpec> createCompiler() {
        NativePlatformInternal targetPlatform = Cast.cast(NativePlatformInternal.class, this.targetPlatform.get());
        NativeToolChainInternal toolChain = Cast.cast(NativeToolChainInternal.class, getToolChain().get());
        PlatformToolProvider toolProvider = toolChain.select(targetPlatform);

    return nullptr;
  }
  DenseElementsAttr quantDenseAttr =
      convertDenseFPElementsAttr(mlir::cast<DenseFPElementsAttr>(realDenseAttr),
                                 quantizedElementType, converter);
  if (!quantDenseAttr) {
    return nullptr;
  }

  // Cast from an expressed-type-based type to storage-type-based type,
  // preserving the sparse shape (i.e. tensor<4xf32> -> tensor<4xi8>).

    if (!mlir::cast<ShapedType>(conv_op.getRhs().getType()).hasStaticShape()) {
      return false;
    }
    if (!mlir::cast<ShapedType>(conv_op.getLhs().getType()).hasStaticShape() &&
        !mlir::cast<ShapedType>(conv_op.getType()).hasStaticShape()) {
      auto dnums = conv_op.getDimensionNumbers();
      auto lhs_type = mlir::cast<ShapedType>(conv_op.getLhs().getType());

// TFR function call defines the signatures of the TF ops.
//
namespace mlir {
namespace TFR {

namespace {

// This pattern is to rewrite the "tfr.call" op and the "tfr.cast" ops on the
// operands by a TF op with "tfr.cast" ops on the results. The result type of
// the new TF op is an unranked tensor with element type derived.
class RewriteTFRCallOp : public OpRewritePattern<CallOp> {

        changes.accept(propertyName, visitor);
        return Cast.uncheckedNonnullCast(visitor.getChanges());
    }

    @Override
    public Iterable<InputFileDetails> getAllFileChanges() {
        CollectingChangeVisitor visitor = new CollectingChangeVisitor();
        changes.accept(visitor);
        return Cast.uncheckedNonnullCast(visitor.getChanges());
    }

        compatibilityRules = Cast.uncheckedCast(instantiatorFactory.decorateLenient().newInstance(DefaultCompatibilityRuleChain.class, instantiatorFactory.inject(), isolatableFactory));

// CHECK-NEXT:        name: "min_runtime_version",
// CHECK-NEXT:        buffer: 4
// CHECK-NEXT:      } ],
// CHECK-NEXT:      signature_defs: [  ]
// CHECK-NEXT:    }

  %0 = "tfl.cast" (%arg0) : (tensor<4x5xbf16>) -> tensor<4x5xf32> loc("cast1")
  %1 = "tfl.cast" (%0) : (tensor<4x5xf32>) -> tensor<4x5xbf16> loc("cast2")
  func.return %1 : tensor<4x5xbf16>

            return DynamicInvokeResult.found(_container.create(name, Cast.<Class<T>>uncheckedCast(params[0])));
        } else if (params.length == 2 && params[0] instanceof Class && params[1] instanceof Closure){
            return DynamicInvokeResult.found(_container.create(name, Cast.uncheckedCast(params[0]), ConfigureUtil.configureUsing((Closure<?>) params[1])));
        }