message = message - sourceFile.absolutePath
                throw new AssertionError(message)
            }
        }

        def diagnosticListener = checkDiagnostic.asType("javax.tools.DiagnosticListener" as Class)

        def input = fileManager.getJavaFileObjectsFromFiles(Arrays.asList(sourceFile))
        compiler.getTask(null,
            fileManager,
            diagnosticListener,

            default:
                throw new UnsupportedOperationException("Generating get call for type: " + upgradedPropertyType.asType() + " is not supported");
        }
    }

    private static CodeBlock getSetCall(String propertyGetterName, PropertyUpgradeRequestExtra implementationExtra, GradleLazyType upgradedPropertyType) {

	vd_aux     uint32 /* Offset in bytes to verdaux array */
	vd_next    uint32 /* Offset in bytes to next verdef entry */
}

type elfEhdr struct {
	e_ident     [_EI_NIDENT]byte /* Magic number and other info */
	e_type      uint16           /* Object file type */
	e_machine   uint16           /* Architecture */
	e_version   uint32           /* Object file version */

}

// A StorageCast (scast) represents a cast from or to a type based on the
// storage type and a type based on a corresponding quantized type.
//
// This op exists to ensure type coherency for between parts of the computation
// which are operating directly on an underlying storage type and those which
// operate on quantized values.
//
// Examples from storage to quantized type:
//   i8 -> !quant<"uniform[i8:f32]{1.0}">

        try (EvaluationContext.ScopeContext context = openScope()) {
            Value<? extends T> value = provider.calculateValue(consumer);
            if (value.isMissing()) {
                return value.asType();
            }
            return doMapValue(context, value).calculateValue(consumer);
        }
    }

        this.keyType = keyType;
        this.valueType = valueType;
        keyCollector = new ValidatingValueCollector<>(Set.class, keyType, ValueSanitizers.forType(keyType));
        entryCollector = new ValidatingMapEntryCollector<>(keyType, valueType, ValueSanitizers.forType(keyType), ValueSanitizers.forType(valueType));
        init();
    }

    private void init() {
        defaultValue = emptySupplier();

namespace mlir {
namespace TFL {
namespace tac {

// Device attribute string on the TFL dialect.
constexpr char kDevice[] = "tac.device";

// Inference type.
constexpr char kInferenceType[] = "tac.inference_type";

// Inference type.
constexpr char kSkipTargetAnnotation[] = "tac.skip_target_annotation";

// TODO(renjieliu): Add more inference types.
enum InferenceType {
  UNKNOWN = 0,
  FLOAT = 1,
  QUANTIZED_INT8 = 2,

        "  rng = np.random.default_rng(seed=1235)\n",
        "  for _ in range(2):\n",
        "    yield {\n",
        "        'lhs_operand': rng.uniform(low=-1.0, high=1.0, size=input_shape).astype(\n",
        "            np.float32\n",
        "        )\n",
        "    }\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)\n",
        "converter.target_spec.supported_ops = [\n",

}

func (m *mockTypeRef) TypeName() string {
	return m.objectType.TypeName()
}

func (m *mockTypeRef) CELType() *types.Type {
	return types.NewTypeTypeWithParam(m.objectType)
}

func (m *mockTypeRef) Field(name string) (*types.FieldType, bool) {
	return &types.FieldType{
		Type: types.DynType,
		IsSet: func(target any) bool {
			return true
		},
		GetFrom: func(target any) (any, error) {
			return nil, nil
		},

Rita Zhang <******@****.***> 1677726350 -0800