val coupledProjects = listOf(taskOwner)
            reportCrossProjectTaskAccess(coupledProjects, task.path)
        }
    }

    override fun hasTask(task: Task): Boolean {
        checkCrossProjectTaskAccess(task)
        return delegate.hasTask(task)
    }

    override fun getAllTasks(): MutableList<Task> {
        val result = delegate.allTasks
        observingTasksMaybeFromOtherProjects(result)

        "graph.hasTask(':b:bTask')"          | [":b:help"]                      | true
        "graph.hasTask(':b:help')"           | [":b:help"]                      | true
        "graph.hasTask(':help')"             | [":b:help"]                      | false
        "graph.hasTask(':x:unknown')"        | [":b:help"]                      | false

	}
	if os.Stdout == nil {
		panic("os.Stdout is nil")
	}

	Initialized = true
}

var Initialized bool

var SlicePtr interface{} = &[]int{}

var V int = 1

var HasMask []string = []string{"hi"}

type HasProg struct {
	array [1024]*byte
}

type Dep struct {
	X int
}

func (d *Dep) Method() int {
	// This code below causes various go.itab.* symbols to be generated in

  // taking the most specialized. This combines `10x?x?` and `?x?x8` into
  // `10x?x8`.
  if (!lhs_shape_type.hasRank()) {
    if (rhs_shape_type.hasRank()) {
      shape.append(rhs_shape_type.getShape().begin(),
                   rhs_shape_type.getShape().end());
      refined_shape = true;
    }
  } else if (rhs_shape_type.hasRank()) {
    for (auto shape_elts : llvm::enumerate(

    auto input_ty = mlir::cast<ShapedType>(input.getType());
    auto min_ty = mlir::cast<ShapedType>(min.getType());
    auto max_ty = mlir::cast<ShapedType>(max.getType());

    if (!input_ty.hasRank() || !min_ty.hasRank() || !max_ty.hasRank()) {
      return failure();
    }

    auto shape = rewriter.create<TF::ShapeOp>(
        op.getLoc(),
        tensorflow::GetTypeFromTFTensorShape({input_ty.getRank()},

  std::vector<int32_t> shape;
  if (tensor_type.hasRank()) {
    llvm::ArrayRef<int64_t> shape_ref = tensor_type.getShape();
    shape = std::vector<int32_t>(shape_ref.begin(), shape_ref.end());
  }

  variant_params.push_back(
      tflite::CreateVariantSubType(builder_, builder_.CreateVector(shape),
                                   tflite_element_type, tensor_type.hasRank()));
  return variant_params;
}

    // Here we assume that the element_shape won't be changed before calling
    // the first `TensorListSetItemOp`.
    if (auto shaped_type = element_shape.getType().dyn_cast<ShapedType>()) {
      if (shaped_type.hasRank() && shaped_type.getRank() == 0) {
        bool element_shape_acquired = false;
        auto uses = op.getResult().getUses();
        for (auto &use : llvm::make_early_inc_range(uses)) {