code: def series_drop_duplicates(
      series, keep="first", inplace=False, method="auto"
  ):
      """
      Return Series with duplicate values removed.
  
      Parameters
      ----------
      keep : {'first', 'last', ``False``}, default 'first'
          Method to handle dropping duplicates:
  
          - 'first' : Drop duplicates except for the first occurrence.
          - 'last' : Drop duplicates except for the last occurrence.
          - ``False`` : Drop all duplicates.
  
      inplace : bool, default ``False``
          If ``True``, performs operation inplace and returns None.
  
      Returns
      -------
      Series
          Series with duplicates dropped.
  
      See Also
      --------
      Index.drop_duplicates : Equivalent method on Index.
      DataFrame.drop_duplicates : Equivalent method on DataFrame.
      Series.duplicated : Related method on Series, indicating duplicate
          Series values.
  
      Examples
      --------
      Generate a Series with duplicated entries.
  
      >>> import mars.dataframe as md
      >>> s = md.Series(['lame', 'cow', 'lame', 'beetle', 'lame', 'hippo'],
      ...               name='animal')
      >>> s.execute()
      0      lame
      1       cow
      2      lame
      3    beetle
      4      lame
      5     hippo
      Name: animal, dtype: object
  
      With the 'keep' parameter, the selection behaviour of duplicated values
      can be changed. The value 'first' keeps the first occurrence for each
      set of duplicated entries. The default value of keep is 'first'.
  
      >>> s.drop_duplicates().execute()
      0      lame
      1       cow
      3    beetle
      5     hippo
      Name: animal, dtype: object
  
      The value 'last' for parameter 'keep' keeps the last occurrence for
      each set of duplicated entries.
  
      >>> s.drop_duplicates(keep='last').execute()
      1       cow
      3    beetle
      4      lame
      5     hippo
      Name: animal, dtype: object
  
      The value ``False`` for parameter 'keep' discards all sets of
      duplicated entries. Setting the value of 'inplace' to ``True`` performs
      the operation inplace and returns ``None``.
  
      >>> s.drop_duplicates(keep=False, inplace=True)
      >>> s.execute()
      1       cow
      3    beetle
      5     hippo
      Name: animal, dtype: object
      """
      if method not in ("auto", "tree", "shuffle", None):
          raise ValueError(
              "method could only be one of 'auto', 'tree', 'shuffle' or None"
          )
      op = DataFrameDropDuplicates(keep=keep, method=method)
      return op(series, inplace=inplace)
  
imports: import numpy
  import pandas
  from ...None import opcodes
  from ...core.operand import OperandStage
  from ...serialization.serializables import BoolField
  from ...utils import lazy_import, calc_nsplits
  from ..operands import OutputType
  from ..utils import parse_index, hash_dataframe_on, gen_unknown_index_value, standardize_range_index
  from ._duplicate import DuplicateOperand, validate_subset
label_desc: Use image node_id 2 for calling a global function with example usage: series_drop_duplicates(series, keep, inplace, method) and returns: op