CHECK-NEXT:   %[[SplitV:.*]] = tfr.call @tf__split_v(%lhs, %[[pack]], %[[cst_5]], %[[cst_4]])
      CHECK-NEXT:   %[[idx:.*]] = arith.constant 0 : index
      CHECK-NEXT:   %[[elt:.*]] = tfr.get_element %SplitV[%idx] : (!tfr.tensor_list, index) -> !tfr.tensor
      CHECK-NEXT:   %[[idx_1:.*]] = arith.constant 1 : index
      CHECK-NEXT:   %[[elt_1:.*]] = tfr.get_element %SplitV[%idx_1] : (!tfr.tensor_list, index) -> !tfr.tensor

  return scope.status();
}
REGISTER_GRADIENT_OP("Split", SplitGrad);

Status SplitVGrad(const Scope& scope, const Operation& op,
                  const std::vector<Output>& grad_inputs,
                  std::vector<Output>* grad_outputs) {
  if (op.num_inputs() < 3) {
    return errors::InvalidArgument("SplitV requires 3 arguments");
  }

}

TEST_F(ArrayGradTest, SplitGrad) {
  TensorShape x_shape({5, 2});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
  // Split along the second dimension.
  auto split_dim = Const(scope_, 1, {});
  auto y = Split(scope_, split_dim, x, /* num_split */ 2);
  TensorShape y_shape = TensorShape({5, 1});
  RunTest({x}, {x_shape}, y.output, {y_shape, y_shape});
}

}

// ================== splitV ========================

LogicalResult UnrollSplitV::matchAndRewrite(TFL::SplitVOp splitv_op,
                                            PatternRewriter& rewriter) const {
  // We need to make sure both splits & split dim are constants.
  auto splits = splitv_op.getSizeSplits().getDefiningOp();
  mlir::DenseIntElementsAttr splits_attr;
  if (!splits || !matchPattern(splits, m_Constant(&splits_attr)))

DECL_CONVERT_OP(BatchMatMulV2);
DECL_CONVERT_OP(BatchMatMulV3);
DECL_CONVERT_OP(MatMul);
DECL_CONVERT_OP(MatrixDiagV2);
DECL_CONVERT_OP(MatrixDiagV3);
DECL_CONVERT_OP(Pack);
DECL_CONVERT_OP(Split);
DECL_CONVERT_OP(SplitV);
DECL_CONVERT_OP(Unpack);
DECL_CONVERT_OP(Conv3D);
DECL_CONVERT_OP(Conv3DBackpropInputV2);

#undef DECL_CONVERT_OP

// Converts any IntegerAttr to an IntegerAttr of an i32 type.

      Spliterator<? extends T> splitr = stream.spliterator();
      splitrsBuilder.add(splitr);
      characteristics &= splitr.characteristics();
      estimatedSize = LongMath.saturatedAdd(estimatedSize, splitr.estimateSize());
    }
    return StreamSupport.stream(
            CollectSpliterators.flatMap(
                splitrsBuilder.build().spliterator(),
                splitr -> (Spliterator<T>) splitr,

    return strategy.iterator(this, sequence);
  }

  /**
   * Splits {@code sequence} into string components and returns them as an immutable list. If you
   * want an {@link Iterable} which may be lazily evaluated, use {@link #split(CharSequence)}.
   *
   * @param sequence the sequence of characters to split
   * @return an immutable list of the segments split from the parameter
   * @since 15.0
   */

    return strategy.iterator(this, sequence);
  }

  /**
   * Splits {@code sequence} into string components and returns them as an immutable list. If you
   * want an {@link Iterable} which may be lazily evaluated, use {@link #split(CharSequence)}.
   *
   * @param sequence the sequence of characters to split
   * @return an immutable list of the segments split from the parameter
   * @since 15.0
   */

	return genSplit(s, sep, len(sep), n)
}

// Split slices s into all substrings separated by sep and returns a slice of
// the substrings between those separators.
//
// If s does not contain sep and sep is not empty, Split returns a
// slice of length 1 whose only element is s.
//
// If sep is empty, Split splits after each UTF-8 sequence. If both s
// and sep are empty, Split returns an empty slice.
//

			Name:     roleName,
		},
	}

	r.Subjects = make([]rbacv1.Subject, len(subjects))
	for i, subject := range subjects {
		split := strings.SplitN(subject, ":", 2)
		r.Subjects[i].Kind, r.Subjects[i].Name = split[0], split[1]

		switch r.Subjects[i].Kind {
		case rbacv1.ServiceAccountKind:
			r.Subjects[i].APIGroup = ""
		case rbacv1.UserKind, rbacv1.GroupKind: