code: def _call_series(
      self, series, dtypes=None, dtype=None, name=None, index=None
  ):
      # for backward compatibility
      dtype = dtype if dtype is not None else dtypes
      if self._convert_dtype:
          if self.output_types is not None and (
              dtypes is not None or dtype is not None
          ):
              infer_series = test_series = None
          else:
              test_series = build_series(
                  series, size=2, name=series.name
              )
              try:
                  with np.errstate(all="ignore"), quiet_stdio():
                      infer_series = test_series.apply(
                          self._func, args=self.args, **self.kwds
                      )
              except:  # noqa: E722  # nosec  # pylint: disable=bare-except
                  infer_series = None
  
          output_type = self._output_types[0]
  
          if index is not None:
              index_value = parse_index(index)
          elif infer_series is not None:
              if infer_series.index is test_series.index:
                  index_value = series.index_value
              else:  # pragma: no cover
                  index_value = parse_index(infer_series.index)
          else:
              index_value = parse_index(None, series)
  
          if output_type == OutputType.dataframe:
              if dtypes is None:
                  if (
                      infer_series is not None
                      and infer_series.ndim == 2
                  ):
                      dtypes = infer_series.dtypes
                  else:
                      raise TypeError(
                          "Cannot determine dtypes, "
                          "please specify `dtypes` as argument"
                      )
              columns_value = parse_index(dtypes.index, store_data=True)
  
              return self.new_dataframe(
                  [series],
                  shape=(series.shape[0], len(dtypes)),
                  index_value=index_value,
                  columns_value=columns_value,
                  dtypes=dtypes,
              )
          else:
              if (
                  dtype is None
                  and infer_series is not None
                  and infer_series.ndim == 1
              ):
                  dtype = infer_series.dtype
              else:
                  dtype = (
                      dtype if dtype is not None else np.dtype(object)
                  )
              if infer_series is not None and infer_series.ndim == 1:
                  name = name or infer_series.name
              return self.new_series(
                  [series],
                  dtype=dtype,
                  shape=series.shape,
                  index_value=index_value,
                  name=name,
              )
      else:
          dtype = dtype if dtype is not None else np.dtype("object")
          return self.new_series(
              [series],
              dtype=dtype,
              shape=series.shape,
              index_value=series.index_value,
              name=name,
          )
  
imports: import inspect
  import numpy
  import pandas
  from ...None import opcodes
  from ...config import options
  from ...core import OutputType, recursive_tile
  from ...core.custom_log import redirect_custom_log
  from ...core.operand import OperatorLogicKeyGeneratorMixin
  from ...serialization.serializables import StringField, AnyField, BoolField, TupleField, DictField, FunctionField
  from ...utils import enter_current_session, quiet_stdio, get_func_token, tokenize
  from ..arrays import ArrowArray
  from ..operands import DataFrameOperandMixin, DataFrameOperand
  from ..utils import build_df, build_series, parse_index, validate_axis, validate_output_types, make_dtypes, make_dtype, build_empty_df, build_empty_series, clean_up_func, restore_func
label_desc: Use image node_id 25 for calling the ApplyOperand obj's underlying member method code with example usage: obj._call_series(series, dtypes, dtype, name, index) and returns: self, self, self