code: def series_drop(
      series,
      labels=None,
      axis=0,
      index=None,
      columns=None,
      level=None,
      inplace=False,
      errors="raise",
  ):
      """
      Return Series with specified index labels removed.
  
      Remove elements of a Series based on specifying the index labels.
      When using a multi-index, labels on different levels can be removed
      by specifying the level.
  
      Parameters
      ----------
      labels : single label or list-like
          Index labels to drop.
      axis : 0, default 0
          Redundant for application on Series.
      index : single label or list-like
          Redundant for application on Series, but 'index' can be used instead
          of 'labels'.
  
          .. versionadded:: 0.21.0
      columns : single label or list-like
          No change is made to the Series; use 'index' or 'labels' instead.
  
          .. versionadded:: 0.21.0
      level : int or level name, optional
          For MultiIndex, level for which the labels will be removed.
      inplace : bool, default False
          If True, do operation inplace and return None.
      errors : {'ignore', 'raise'}, default 'raise'
          Note that this argument is kept only for compatibility, and errors
          will not raise even if ``errors=='raise'``.
  
      Returns
      -------
      Series
          Series with specified index labels removed.
  
      Raises
      ------
      KeyError
          If none of the labels are found in the index.
  
      See Also
      --------
      Series.reindex : Return only specified index labels of Series.
      Series.dropna : Return series without null values.
      Series.drop_duplicates : Return Series with duplicate values removed.
      DataFrame.drop : Drop specified labels from rows or columns.
  
      Examples
      --------
      >>> import numpy as np
      >>> import pandas as pd
      >>> import mars.dataframe as md
      >>> s = md.Series(data=np.arange(3), index=['A', 'B', 'C'])
      >>> s.execute()
      A  0
      B  1
      C  2
      dtype: int64
  
      Drop labels B en C
  
      >>> s.drop(labels=['B', 'C']).execute()
      A  0
      dtype: int64
  
      Drop 2nd level label in MultiIndex Series
  
      >>> midx = pd.MultiIndex(levels=[['lame', 'cow', 'falcon'],
      ...                              ['speed', 'weight', 'length']],
      ...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
      ...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
      >>> s = md.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
      ...               index=midx)
      >>> s.execute()
      lame    speed      45.0
              weight    200.0
              length      1.2
      cow     speed      30.0
              weight    250.0
              length      1.5
      falcon  speed     320.0
              weight      1.0
              length      0.3
      dtype: float64
  
      >>> s.drop(labels='weight', level=1).execute()
      lame    speed      45.0
              length      1.2
      cow     speed      30.0
              length      1.5
      falcon  speed     320.0
              length      0.3
      dtype: float64
      """
      return _drop(
          series,
          labels=labels,
          axis=axis,
          index=index,
          columns=columns,
          level=level,
          inplace=inplace,
          errors=errors,
      )
  
imports: import warnings
  from collections import OrderedDict
  import numpy
  from ...None import opcodes
  from ...core import Entity, Chunk, CHUNK_TYPE, OutputType, recursive_tile
  from ...serialization.serializables import AnyField, StringField
  from ..core import IndexValue, DATAFRAME_TYPE, SERIES_TYPE, INDEX_CHUNK_TYPE
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import parse_index, validate_axis
label_desc: Use image node_id 4 for calling a global function with example usage: series_drop(series, labels, axis, index, columns, level, inplace, errors) and returns: _drop