for {
		val := auxIntToInt16(v.AuxInt)
		v.reset(OpRISCV64MOVDconst)
		v.AuxInt = int64ToAuxInt(int64(val))
		return true
	}
}
func rewriteValueRISCV64_OpConst32(v *Value) bool {
	// match: (Const32 [val])
	// result: (MOVDconst [int64(val)])
	for {
		val := auxIntToInt32(v.AuxInt)
		v.reset(OpRISCV64MOVDconst)
		v.AuxInt = int64ToAuxInt(int64(val))
		return true
	}
}

    }

    for (auto i = 0; i < window_strides.size(); ++i) {
      window_strides_vec.push_back(
          window_strides.getValues<IntegerAttr>()[i].getInt());
    }

    for (auto i = 0; i < base_dilations.size(); ++i) {
      base_dilations_vec.push_back(
          base_dilations.getValues<IntegerAttr>()[i].getInt());
    }

    inferredReturnTypes.assign({result_type});
    return success();
  }

  auto stride_h = op.getStrideHAttr().getInt();
  auto stride_w = op.getStrideWAttr().getInt();
  auto dilation_h = op.getDilationHFactorAttr().getInt();
  auto dilation_w = op.getDilationWFactorAttr().getInt();

  // We don't have EXPLICIT PADDING in TfLite.
  auto paddings = op.getPadding();
  tensorflow::Padding padding;

    // Canonicalizes the given range `point` (begin/end) according to the
    // current dimension. `c` means case: 0 for begin, 1 for end.
    auto canonicalize = [&](int64_t point, int c) {
      if (masks[c]) return stride_i > 0 ? bounds[c] : bounds[(c + 1) & 1];

      // Add dim as offset to negative range point.
      point = point < 0 ? dim_i + point : point;
      return Clamp(point, bounds[0], bounds[1]);
    };

(IsInBounds (ZeroExt16to64 (Rsh16Ux64 _ (Const64 [c]))) (Const64 [d])) && 0 < c && c < 16 && 1<<uint(16-c)-1 < d => (ConstBool [true])
(IsInBounds (ZeroExt16to32 (Rsh16Ux64 _ (Const64 [c]))) (Const64 [d])) && 0 < c && c < 16 && 1<<uint(16-c)-1 < d => (ConstBool [true])
(IsInBounds                (Rsh16Ux64 _ (Const64 [c]))  (Const64 [d])) && 0 < c && c < 16 && 1<<uint(16-c)-1 < d => (ConstBool [true])

		switch Kind(dst.Kind()) {
		case Int, Int8, Int16, Int32, Int64, Uint, Uint8, Uint16, Uint32, Uint64, Uintptr:
			return cvtInt
		case Float32, Float64:
			return cvtIntFloat
		case String:
			return cvtIntString
		}

	case Uint, Uint8, Uint16, Uint32, Uint64, Uintptr:
		switch Kind(dst.Kind()) {
		case Int, Int8, Int16, Int32, Int64, Uint, Uint8, Uint16, Uint32, Uint64, Uintptr:
			return cvtUint

</p>

<pre>
[...]Point{{1.5, -3.5}, {0, 0}}     // same as [...]Point{Point{1.5, -3.5}, Point{0, 0}}
[][]int{{1, 2, 3}, {4, 5}}          // same as [][]int{[]int{1, 2, 3}, []int{4, 5}}
[][]Point{{{0, 1}, {1, 2}}}         // same as [][]Point{[]Point{Point{0, 1}, Point{1, 2}}}
map[string]Point{"orig": {0, 0}}    // same as map[string]Point{"orig": Point{0, 0}}

OperandRange GeneratorDatasetRegionOp::getEntrySuccessorOperands(
    RegionBranchPoint point) {
  auto end = this->getOperation()->operand_end();
  if (point.isParent()) {
    // The op itself doesn't branch back to itself.
    return ::mlir::OperandRange(end, end);
  } else if (point.getRegionOrNull() == &getInit()) {
    return getInitFuncOtherArgs();
  } else if (point.getRegionOrNull() == &getNext()) {
    return getNextFuncOtherArgs();

  And<[
    Or<[CPred<"getElementTypeOrSelf($_op.getResult(" # i # ")).isa<mlir::TF::Quint" # num # "Type>()">,
        CPred<"getElementTypeOrSelf($_op.getResult(" # i # ")).isUnsignedInteger(" # num # ")">]>,
    Or<[CPred<"getElementTypeOrSelf($_op.getOperand(" # j # ")).isa<mlir::TF::Quint" # num # "Type>()">,
        CPred<"getElementTypeOrSelf($_op.getOperand(" # j # ")).isUnsignedInteger(" # num # ")">]>]>;