code: def union_with_duplicates(
      lvals: ArrayLike | Index, rvals: ArrayLike | Index
  ) -> ArrayLike | Index:
      """
      Extracts the union from lvals and rvals with respect to duplicates and nans in
      both arrays.
  
      Parameters
      ----------
      lvals: np.ndarray or ExtensionArray
          left values which is ordered in front.
      rvals: np.ndarray or ExtensionArray
          right values ordered after lvals.
  
      Returns
      -------
      np.ndarray or ExtensionArray
          Containing the unsorted union of both arrays.
  
      Notes
      -----
      Caller is responsible for ensuring lvals.dtype == rvals.dtype.
      """
      from pandas import Series
  
      with warnings.catch_warnings():
          # filter warning from object dtype inference; we will end up discarding
          # the index here, so the deprecation does not affect the end result here.
          warnings.filterwarnings(
              "ignore",
              "The behavior of value_counts with object-dtype is deprecated",
              category=FutureWarning,
          )
          l_count = value_counts_internal(lvals, dropna=False)
          r_count = value_counts_internal(rvals, dropna=False)
      l_count, r_count = l_count.align(r_count, fill_value=0)
      final_count = np.maximum(l_count.values, r_count.values)
      final_count = Series(
          final_count, index=l_count.index, dtype="int", copy=False
      )
      if isinstance(lvals, ABCMultiIndex) and isinstance(
          rvals, ABCMultiIndex
      ):
          unique_vals = lvals.append(rvals).unique()
      else:
          if isinstance(lvals, ABCIndex):
              lvals = lvals._values
          if isinstance(rvals, ABCIndex):
              rvals = rvals._values
          # error: List item 0 has incompatible type "Union[ExtensionArray,
          # ndarray[Any, Any], Index]"; expected "Union[ExtensionArray,
          # ndarray[Any, Any]]"
          combined = concat_compat([lvals, rvals])  # type: ignore[list-item]
          unique_vals = unique(combined)
          unique_vals = ensure_wrapped_if_datetimelike(unique_vals)
      repeats = final_count.reindex(unique_vals).values
      return np.repeat(unique_vals, repeats)
  
imports: from __future__ import annotations
  import decimal
  import operator
  from textwrap import dedent
  from typing import TYPE_CHECKING, Literal, cast
  import warnings
  import numpy
  from pandas._libs import algos, hashtable, iNaT, lib
  from pandas._typing import AnyArrayLike, ArrayLike, ArrayLikeT, AxisInt, DtypeObj, TakeIndexer, npt
  from pandas.util._decorators import doc
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike, np_find_common_type
  from pandas.core.dtypes.common import ensure_float64, ensure_object, ensure_platform_int, is_array_like, is_bool_dtype, is_complex_dtype, is_dict_like, is_extension_array_dtype, is_float, is_float_dtype, is_integer, is_integer_dtype, is_list_like, is_object_dtype, is_signed_integer_dtype, needs_i8_conversion
  from pandas.core.dtypes.concat import concat_compat
  from pandas.core.dtypes.dtypes import BaseMaskedDtype, CategoricalDtype, ExtensionDtype, NumpyEADtype
  from pandas.core.dtypes.generic import ABCDatetimeArray, ABCExtensionArray, ABCIndex, ABCMultiIndex, ABCSeries, ABCTimedeltaArray
  from pandas.core.dtypes.missing import isna, na_value_for_dtype
  from pandas.core.array_algos.take import take_nd
  from pandas.core.construction import array, ensure_wrapped_if_datetimelike, extract_array
  from pandas.core.indexers import validate_indices
label_desc: Use image node_id 24 for calling a global function with example usage: union_with_duplicates(lvals, rvals) and returns: np