code: def get_empty_dtype_and_na(join_units):
      """
      Return dtype and N/A values to use when concatenating specified units.
  
      Returned N/A value may be None which means there was no casting involved.
  
      Returns
      -------
      dtype
      na
      """
      if len(join_units) == 1:
          blk = join_units[0].block
          if blk is None:
              return np.float64, np.nan
  
      if is_uniform_reindex(join_units):
          # XXX: integrate property
          empty_dtype = join_units[0].block.dtype
          upcasted_na = join_units[0].block.fill_value
          return empty_dtype, upcasted_na
  
      has_none_blocks = False
      dtypes = [None] * len(join_units)
      for i, unit in enumerate(join_units):
          if unit.block is None:
              has_none_blocks = True
          else:
              dtypes[i] = unit.dtype
  
      upcast_classes = defaultdict(list)
      null_upcast_classes = defaultdict(list)
      for dtype, unit in zip(dtypes, join_units):
          if dtype is None:
              continue
  
          if is_categorical_dtype(dtype):
              upcast_cls = "category"
          elif is_datetime64tz_dtype(dtype):
              upcast_cls = "datetimetz"
          elif issubclass(dtype.type, np.bool_):
              upcast_cls = "bool"
          elif issubclass(dtype.type, np.object_):
              upcast_cls = "object"
          elif is_datetime64_dtype(dtype):
              upcast_cls = "datetime"
          elif is_timedelta64_dtype(dtype):
              upcast_cls = "timedelta"
          elif is_sparse(dtype):
              upcast_cls = dtype.subtype.name
          elif is_extension_array_dtype(dtype):
              upcast_cls = "object"
          elif is_float_dtype(dtype) or is_numeric_dtype(dtype):
              upcast_cls = dtype.name
          else:
              upcast_cls = "float"
  
          # Null blocks should not influence upcast class selection, unless there
          # are only null blocks, when same upcasting rules must be applied to
          # null upcast classes.
          if unit.is_na:
              null_upcast_classes[upcast_cls].append(dtype)
          else:
              upcast_classes[upcast_cls].append(dtype)
  
      if not upcast_classes:
          upcast_classes = null_upcast_classes
  
      # create the result
      if "object" in upcast_classes:
          return np.dtype(np.object_), np.nan
      elif "bool" in upcast_classes:
          if has_none_blocks:
              return np.dtype(np.object_), np.nan
          else:
              return np.dtype(np.bool_), None
      elif "category" in upcast_classes:
          return np.dtype(np.object_), np.nan
      elif "datetimetz" in upcast_classes:
          # GH-25014. We use NaT instead of iNaT, since this eventually
          # ends up in DatetimeArray.take, which does not allow iNaT.
          dtype = upcast_classes["datetimetz"]
          return dtype[0], tslibs.NaT
      elif "datetime" in upcast_classes:
          return np.dtype("M8[ns]"), tslibs.iNaT
      elif "timedelta" in upcast_classes:
          return np.dtype("m8[ns]"), tslibs.iNaT
      else:  # pragma
          try:
              g = np.find_common_type(upcast_classes, [])
          except TypeError:
              # At least one is an ExtensionArray
              return np.dtype(np.object_), np.nan
          else:
              if is_float_dtype(g):
                  return g, g.type(np.nan)
              elif is_numeric_dtype(g):
                  if has_none_blocks:
                      return np.float64, np.nan
                  else:
                      return g, None
  
      msg = "invalid dtype determination in get_concat_dtype"
      raise AssertionError(msg)
  
imports: from collections import defaultdict
  import copy
  import numpy
  from pandas._libs import internals, tslibs
  from pandas.util._decorators import cache_readonly
  from pandas.core.dtypes.cast import maybe_promote
  from pandas.core.dtypes.common import _get_dtype, is_categorical_dtype, is_datetime64_dtype, is_datetime64tz_dtype, is_extension_array_dtype, is_float_dtype, is_numeric_dtype, is_sparse, is_timedelta64_dtype
  import pandas.core.dtypes.concat
  from pandas.core.dtypes.missing import isna
  import pandas.core.algorithms
label_desc: Use image node_id 3 for calling a global function with example usage: get_empty_dtype_and_na(join_units) and returns: empty_dtype,  upcasted_na, np,  np, np,  np, np,  np, np,  None, np,  np, dtype,  tslibs, np,  tslibs, np,  tslibs, np,  np, g,  g, np,  np, g,  None