code: def df_drop_duplicates(
      df,
      subset=None,
      keep="first",
      inplace=False,
      ignore_index=False,
      method="auto",
  ):
      """
      Return DataFrame with duplicate rows removed.
  
      Considering certain columns is optional. Indexes, including time indexes
      are ignored.
  
      Parameters
      ----------
      subset : column label or sequence of labels, optional
          Only consider certain columns for identifying duplicates, by
          default use all of the columns.
      keep : {'first', 'last', False}, default 'first'
          Determines which duplicates (if any) to keep.
          - ``first`` : Drop duplicates except for the first occurrence.
          - ``last`` : Drop duplicates except for the last occurrence.
          - False : Drop all duplicates.
      inplace : bool, default False
          Whether to drop duplicates in place or to return a copy.
      ignore_index : bool, default False
          If True, the resulting axis will be labeled 0, 1, …, n - 1.
  
      Returns
      -------
      DataFrame
          DataFrame with duplicates removed or None if ``inplace=True``.
      """
      if method not in ("auto", "tree", "subset_tree", "shuffle", None):
          raise ValueError(
              "method could only be one of "
              "'auto', 'tree', 'subset_tree', 'shuffle' or None"
          )
      subset = validate_subset(df, subset)
      op = DataFrameDropDuplicates(
          subset=subset,
          keep=keep,
          ignore_index=ignore_index,
          method=method,
      )
      return op(df, 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 1 for calling a global function with example usage: df_drop_duplicates(df, subset, keep, inplace, ignore_index, method) and returns: op