code: def df_drop(
      df,
      labels=None,
      axis=0,
      index=None,
      columns=None,
      level=None,
      inplace=False,
      errors="raise",
  ):
      """
      Drop specified labels from rows or columns.
  
      Remove rows or columns by specifying label names and corresponding
      axis, or by specifying directly index or column names. When using a
      multi-index, labels on different levels can be removed by specifying
      the level.
  
      Parameters
      ----------
      labels : single label or list-like
          Index or column labels to drop.
      axis : {0 or 'index', 1 or 'columns'}, default 0
          Whether to drop labels from the index (0 or 'index') or
          columns (1 or 'columns').
      index : single label or list-like
          Alternative to specifying axis (``labels, axis=0``
          is equivalent to ``index=labels``).
      columns : single label or list-like
          Alternative to specifying axis (``labels, axis=1``
          is equivalent to ``columns=labels``).
      level : int or level name, optional
          For MultiIndex, level from which the labels will be removed.
      inplace : bool, default False
          If True, do operation inplace and return None.
      errors : {'ignore', 'raise'}, default 'raise'
          If 'ignore', suppress error and only existing labels are
          dropped. Note that errors for missing indices will not raise.
  
      Returns
      -------
      DataFrame
          DataFrame without the removed index or column labels.
  
      Raises
      ------
      KeyError
          If any of the labels is not found in the selected axis.
  
      See Also
      --------
      DataFrame.loc : Label-location based indexer for selection by label.
      DataFrame.dropna : Return DataFrame with labels on given axis omitted
          where (all or any) data are missing.
      DataFrame.drop_duplicates : Return DataFrame with duplicate rows
          removed, optionally only considering certain columns.
      Series.drop : Return Series with specified index labels removed.
  
      Examples
      --------
      >>> import numpy as np
      >>> import pandas as pd
      >>> import mars.dataframe as md
      >>> df = md.DataFrame(np.arange(12).reshape(3, 4),
      ...                   columns=['A', 'B', 'C', 'D'])
      >>> df.execute()
         A  B   C   D
      0  0  1   2   3
      1  4  5   6   7
      2  8  9  10  11
  
      Drop columns
  
      >>> df.drop(['B', 'C'], axis=1).execute()
         A   D
      0  0   3
      1  4   7
      2  8  11
  
      >>> df.drop(columns=['B', 'C']).execute()
         A   D
      0  0   3
      1  4   7
      2  8  11
  
      Drop a row by index
  
      >>> df.drop([0, 1]).execute()
         A  B   C   D
      2  8  9  10  11
  
      Drop columns and/or rows of MultiIndex DataFrame
  
      >>> 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]])
      >>> df = md.DataFrame(index=midx, columns=['big', 'small'],
      ...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
      ...                         [250, 150], [1.5, 0.8], [320, 250],
      ...                         [1, 0.8], [0.3, 0.2]])
      >>> df.execute()
                      big     small
      lame    speed   45.0    30.0
              weight  200.0   100.0
              length  1.5     1.0
      cow     speed   30.0    20.0
              weight  250.0   150.0
              length  1.5     0.8
      falcon  speed   320.0   250.0
              weight  1.0     0.8
              length  0.3     0.2
  
      >>> df.drop(index='cow', columns='small').execute()
                      big
      lame    speed   45.0
              weight  200.0
              length  1.5
      falcon  speed   320.0
              weight  1.0
              length  0.3
  
      >>> df.drop(index='length', level=1).execute()
                      big     small
      lame    speed   45.0    30.0
              weight  200.0   100.0
      cow     speed   30.0    20.0
              weight  250.0   150.0
      falcon  speed   320.0   250.0
              weight  1.0     0.8
      """
      return _drop(
          df,
          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 2 for calling a global function with example usage: df_drop(df, labels, axis, index, columns, level, inplace, errors) and returns: _drop