elem_type.isF32()) {
    zero = builder.getFloatAttr(elem_type, 0);
  } else {
    return false;
  }
  if (auto ranked_type = dyn_cast<RankedTensorType>(type)) {
    llvm::ArrayRef<int64_t> type_shape = ranked_type.getShape();
    for (int64_t i : type_shape) {
      if (i < 0) return false;
    }
    shape = builder.getI64TensorAttr(type_shape);
  } else {
    return false;

    // also populate the shape info to the op result. The shape of the
    // tensorlist is inferred from `num_elements` and `element_shape`.
    auto ranked_type = element_shape.getType().dyn_cast<RankedTensorType>();
    DenseIntElementsAttr dense_elem_attr;
    if ((ranked_type && ranked_type.getRank() == 0) ||
        !matchPattern(element_shape, m_Constant(&dense_elem_attr))) {
      // If no constant is spotted, just forward the operand.

RankedTensorType DropFirstDimension(Type type) {
  RankedTensorType ranked_type = mlir::dyn_cast<RankedTensorType>(type);
  if (!ranked_type) return {};
  llvm::ArrayRef<int64_t> dims_except_first =
      ranked_type.getShape().drop_front();
  return tensorflow::GetTypeFromTFTensorShape(dims_except_first,
                                              ranked_type.getElementType());
}

// the given type.
TensorType UpdateElementTypeTo(Type ty, Type element_ty) {
  auto ranked_ty = mlir::dyn_cast<RankedTensorType>(ty);
  if (!ranked_ty) {
    return UnrankedTensorType::get(element_ty);
  }
  return RankedTensorType::get(ranked_ty.getShape(), element_ty,
                               ranked_ty.getEncoding());
}

template <typename SrcOpT, typename DstOpT>