code: def get_reindexed_values(
      self, empty_dtype: DtypeObj, upcasted_na
  ) -> ArrayLike:
      values: ArrayLike
  
      if upcasted_na is None and self.block.dtype.kind != "V":
          # No upcasting is necessary
          fill_value = self.block.fill_value
          values = self.block.values
      else:
          fill_value = upcasted_na
  
          if self._is_valid_na_for(empty_dtype):
              # note: always holds when self.block.dtype.kind == "V"
              blk_dtype = self.block.dtype
  
              if blk_dtype == np.dtype("object"):
                  # we want to avoid filling with np.nan if we are
                  # using None; we already know that we are all
                  # nulls
                  values = self.block.values.ravel(order="K")
                  if len(values) and values[0] is None:
                      fill_value = None
  
              if isinstance(empty_dtype, DatetimeTZDtype):
                  # NB: exclude e.g. pyarrow[dt64tz] dtypes
                  i8values = np.full(self.shape, fill_value._value)
                  return DatetimeArray(i8values, dtype=empty_dtype)
  
              elif is_1d_only_ea_dtype(empty_dtype):
                  if (
                      is_dtype_equal(blk_dtype, empty_dtype)
                      and self.indexers
                  ):
                      # avoid creating new empty array if we already have an array
                      # with correct dtype that can be reindexed
                      pass
                  else:
                      empty_dtype = cast(ExtensionDtype, empty_dtype)
                      cls = empty_dtype.construct_array_type()
  
                      missing_arr = cls._from_sequence(
                          [], dtype=empty_dtype
                      )
                      ncols, nrows = self.shape
                      assert ncols == 1, ncols
                      empty_arr = -1 * np.ones((nrows,), dtype=np.intp)
                      return missing_arr.take(
                          empty_arr,
                          allow_fill=True,
                          fill_value=fill_value,
                      )
              elif isinstance(empty_dtype, ExtensionDtype):
                  # TODO: no tests get here, a handful would if we disabled
                  #  the dt64tz special-case above (which is faster)
                  cls = empty_dtype.construct_array_type()
                  missing_arr = cls._empty(
                      shape=self.shape, dtype=empty_dtype
                  )
                  missing_arr[:] = fill_value
                  return missing_arr
              else:
                  # NB: we should never get here with empty_dtype integer or bool;
                  #  if we did, the missing_arr.fill would cast to gibberish
                  missing_arr = np.empty(self.shape, dtype=empty_dtype)
                  missing_arr.fill(fill_value)
                  return missing_arr
  
          if (not self.indexers) and (not self.block._can_consolidate):
              # preserve these for validation in concat_compat
              return self.block.values
  
          if self.block.is_bool:
              # External code requested filling/upcasting, bool values must
              # be upcasted to object to avoid being upcasted to numeric.
              values = self.block.astype(np.dtype("object")).values
          else:
              # No dtype upcasting is done here, it will be performed during
              # concatenation itself.
              values = self.block.values
  
      if not self.indexers:
          # If there's no indexing to be done, we want to signal outside
          # code that this array must be copied explicitly.  This is done
          # by returning a view and checking `retval.base`.
          values = values.view()
  
      else:
          for ax, indexer in self.indexers.items():
              values = algos.take_nd(values, indexer, axis=ax)
  
      return values
  
imports: from __future__ import annotations
  import copy
  import itertools
  from typing import TYPE_CHECKING, Sequence, cast
  import numpy
  from pandas._libs import NaT, internals
  from pandas._libs.missing import NA
  from pandas._typing import ArrayLike, AxisInt, DtypeObj, Manager, Shape
  from pandas.util._decorators import cache_readonly
  from pandas.core.dtypes.astype import astype_array
  from pandas.core.dtypes.cast import ensure_dtype_can_hold_na, find_common_type, np_find_common_type
  from pandas.core.dtypes.common import is_1d_only_ea_dtype, is_dtype_equal, is_scalar, needs_i8_conversion
  from pandas.core.dtypes.concat import concat_compat
  from pandas.core.dtypes.dtypes import DatetimeTZDtype, ExtensionDtype
  from pandas.core.dtypes.missing import is_valid_na_for_dtype, isna, isna_all
  import pandas.core.algorithms
  from pandas.core.arrays import DatetimeArray, ExtensionArray
  from pandas.core.arrays.sparse import SparseDtype
  from pandas.core.construction import ensure_wrapped_if_datetimelike
  from pandas.core.internals.array_manager import ArrayManager, NullArrayProxy
  from pandas.core.internals.blocks import ensure_block_shape, new_block_2d
  from pandas.core.internals.managers import BlockManager
label_desc: Use image node_id 7 for calling the JoinUnit obj's underlying member method code with example usage: obj.get_reindexed_values(empty_dtype, upcasted_na) and returns: values, self, DatetimeArray, missing_arr, missing_arr, missing_arr