e:"css-masks",browsers:["and_chr 114","and_uc 15.5","chrome 109","chrome 113","chrome 114","edge 114","opera 99","samsung 21"]},mask:{feature:"css-masks",browsers:["and_chr 114","and_uc 15.5","chrome 109","chrome 113","chrome 114","edge 114","opera 99","samsung 21"]},"mask-position":{feature:"css-masks",browsers:["and_chr 114","and_uc 15.5","chrome 109","chrome 113","chrome 114","edge 114","opera 99","samsung 21"]},"mask-size":{feature:"css-masks",browsers:["and_chr 114","and_uc 15.5","chrome 109","chrome...

    // Output shape needs to be static to apply 'new_axis_mask' or
    // 'shrink_axis_mask' by reshaping tensor after slice.
    //
    // TODO(hinsu): Relax this constraint for ops without the above masks.
    auto result_ty = mlir::dyn_cast<RankedTensorType>(op.getType());
    if (!result_ty || !result_ty.hasStaticShape()) return failure();

    DenseIntElementsAttr sparse_begin_attr, sparse_end_attr;

		// In that case (handled below), we emit a bound check (and Spectre mask)
		// and then the prove pass will remove the bounds check.
		// In theory the prove pass could potentially remove certain
		// Spectre masks, but it's very delicate and probably better
		// to be conservative and leave them all in.
		return idx
	}

	bNext := s.f.NewBlock(ssa.BlockPlain)
	bPanic := s.f.NewBlock(ssa.BlockExit)