}

// Shuffle ranked tensor dimensions according to the permutation.
Type ShuffleRankedTensorType(Type type, ArrayRef<int64_t> permutation) {
  if (auto ranked_type = mlir::dyn_cast<RankedTensorType>(type)) {
    ArrayRef<int64_t> shape = ranked_type.getShape();
    assert(permutation.size() == shape.size());

    SmallVector<int64_t, 4> new_shape(permutation.size());
    for (size_t i = 0; i < permutation.size(); ++i)

    } else {
      return UnrankedTensorType::get(builder->getI1Type());
    }
  }

  auto ranked_type = mlir::dyn_cast<RankedTensorType>(result_type);
  if (!ranked_type) return UnrankedTensorType::get(builder->getI1Type());

  return RankedTensorType::get(ranked_type.getShape(), builder->getI1Type());
}

Type InferReductionOpType(Value input, Value reduction_indices,

    if (dim != 1) return false;
  }
  return true;
}

bool IsDimensionsDegenerateExceptLastOne(TypedAttr val) {
  if (auto ranked_type = mlir::dyn_cast<RankedTensorType>(val.getType())) {
    return IsDimensionsDegenerateExceptLastOne(ranked_type.getShape());
  }
  return false;
}

}  // namespace TFL

// dimensions.
bool IsBounded(Type ty) {
  auto ranked_ty = mlir::dyn_cast<RankedTensorType>(ty);
  if (!ranked_ty) return false;

  if (ranked_ty.hasStaticShape()) return true;

  auto encoding =
      mlir::dyn_cast_or_null<TypeExtensionsAttr>(ranked_ty.getEncoding());
  if (!encoding) return false;

  for (int i = 0; i < ranked_ty.getRank(); ++i) {
    if (ranked_ty.isDynamicDim(i) &&