// If nil, the top-level Value function is used to generate them.
	Values func([]reflect.Value, *rand.Rand)
}

var defaultConfig Config

// getRand returns the *rand.Rand to use for a given Config.
func (c *Config) getRand() *rand.Rand {
	if c.Rand == nil {
		return rand.New(rand.NewSource(time.Now().UnixNano()))
	}
	return c.Rand
}

    data_rank = then_tensor.getRank();
    if (then_tensor.getRank() > 0)
      data_first_dim = then_tensor.getShape().front();
    if (else_tensor.getRank() > 0)
      data_first_dim = std::max(else_tensor.getShape().front(), data_first_dim);
  } else if (then_has_rank) {
    data_rank = then_tensor.getRank();
    if (then_tensor.getRank() > 0)
      data_first_dim = then_tensor.getShape().front();

  if (!output_ty) return success();

  int64_t expected_output_rank = std::max(x_ty.getRank(), y_ty.getRank());
  if (output_ty.getRank() != expected_output_rank) {
    return op.emitOpError()
           << "found invalid output rank, expected " << expected_output_rank
           << " but got " << output_ty.getRank();
  }

  // Check output batch dim with potential broadcasting.

    auto reshape_input_ty = mlir::dyn_cast<ShapedType>(reshape_input.getType());
    if (!reshape_input_ty.hasStaticShape() || input_ty.getRank() == 0 ||
        reshape_input_ty.getRank() == 0 ||
        input_ty.getDimSize(input_ty.getRank() - 1) !=
            reshape_input_ty.getDimSize(reshape_input_ty.getRank() - 1)) {
      return failure();
    }

    // Connect the input to the one of reshape.

// Converts the condition for an IfOp/WhileOp to a boolean value.
Value ConvertConditionToBoolean(Operation* op, Value cond) {
  if (auto ranked_type = mlir::dyn_cast<RankedTensorType>(cond.getType()))
    if (ranked_type.getRank() == 0 &&
        ranked_type.getElementType().isSignlessInteger(1))
      return cond;

  OpBuilder builder(op);
  Value to_bool = builder.create<TF::ToBoolOp>(op->getLoc(), cond);

			t.Fatalf("expected mytest, got %s", obj.GetName())
		}
		if obj.GetAPIVersion() != apiVersion {
			t.Fatalf("expected %s, got %s", apiVersion, obj.GetAPIVersion())
		}
		if obj.GetKind() != kind {
			t.Fatalf("expected %s, got %s", kind, obj.GetKind())
		}
		if v, ok, err := unstructured.NestedFloat64(obj.Object, "values", "numVal"); v != 1 || !ok || err != nil {
			t.Fatal(v, ok, err, string(result))
		}

        String callableNameForDocComment = interceptedCallable.getKind() == CallableKindInfo.AFTER_CONSTRUCTOR ? className.simpleName() : interceptedCallable.getCallableName();
        List<ParameterInfo> params = request.getInterceptedCallable().getParameters();
        List<ParameterInfo> methodParameters = params.stream().filter(parameter -> parameter.getKind().isSourceParameter()).collect(Collectors.toList());

      : AbstractTensorHandle(kind) {}

 public:
  // For LLVM style RTTI.
  static bool classof(const AbstractTensorHandle* ptr) {
    return ptr->getKind() == kGraph || ptr->getKind() == kMlir;
  }
};

// An abstract operation describes an operation by its type, name, and
// attributes. It can be "executed" by the context with some input tensors.

func FuzzHeaderMatcher(data []byte) int {
	k, v, err := getKandV(data)
	if err != nil {
		return 0
	}
	_ = matcher.HeaderMatcher(k, v)
	return 1
}

func FuzzHostMatcherWithRegex(data []byte) int {
	k, v, err := getKandV(data)
	if err != nil {
		return 0
	}
	_ = matcher.HostMatcherWithRegex(k, v)
	return 1
}

func FuzzHostMatcher(data []byte) int {
	k, v, err := getKandV(data)
	if err != nil {
		return 0
	}

        }
    }

    private static boolean hasGroovyPropertyRequests(List<CallInterceptionRequest> requests) {
        return requests.stream().anyMatch(it -> it.getInterceptedCallable().getKind() == GROOVY_PROPERTY_GETTER || it.getInterceptedCallable().getKind() == GROOVY_PROPERTY_SETTER);
    }

    @SuppressWarnings("ReturnValueIgnored")
    private static CodeBlock interceptorClassJavadoc(Collection<CallInterceptionRequest> requests) {