code: def df_eval(df, expr, inplace=False, **kwargs):
      """
      Evaluate a string describing operations on DataFrame columns.
  
      Operates on columns only, not specific rows or elements.  This allows
      `eval` to run arbitrary code, which can make you vulnerable to code
      injection if you pass user input to this function.
  
      Parameters
      ----------
      expr : str
          The expression string to evaluate.
      inplace : bool, default False
          If the expression contains an assignment, whether to perform the
          operation inplace and mutate the existing DataFrame. Otherwise,
          a new DataFrame is returned.
      **kwargs
          See the documentation for :func:`eval` for complete details
          on the keyword arguments accepted by
          :meth:`~pandas.DataFrame.query`.
  
      Returns
      -------
      ndarray, scalar, or pandas object
          The result of the evaluation.
  
      See Also
      --------
      DataFrame.query : Evaluates a boolean expression to query the columns
          of a frame.
      DataFrame.assign : Can evaluate an expression or function to create new
          values for a column.
      eval : Evaluate a Python expression as a string using various
          backends.
  
      Notes
      -----
      For more details see the API documentation for :func:`~eval`.
      For detailed examples see :ref:`enhancing performance with eval
      <enhancingperf.eval>`.
  
      Examples
      --------
      >>> import mars.dataframe as md
      >>> df = md.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
      >>> df.execute()
         A   B
      0  1  10
      1  2   8
      2  3   6
      3  4   4
      4  5   2
      >>> df.eval('A + B').execute()
      0    11
      1    10
      2     9
      3     8
      4     7
      dtype: int64
  
      Assignment is allowed though by default the original DataFrame is not
      modified.
  
      >>> df.eval('C = A + B').execute()
         A   B   C
      0  1  10  11
      1  2   8  10
      2  3   6   9
      3  4   4   8
      4  5   2   7
      >>> df.execute()
         A   B
      0  1  10
      1  2   8
      2  3   6
      3  4   4
      4  5   2
  
      Use ``inplace=True`` to modify the original DataFrame.
  
      >>> df.eval('C = A + B', inplace=True)
      >>> df.execute()
         A   B   C
      0  1  10  11
      1  2   8  10
      2  3   6   9
      3  4   4   8
      4  5   2   7
  
      Multiple columns can be assigned to using multi-line expressions:
  
      >>> df.eval('''
      ... C = A + B
      ... D = A - B
      ... ''').execute()
         A   B   C  D
      0  1  10  11 -9
      1  2   8  10 -6
      2  3   6   9 -3
      3  4   4   8  0
      4  5   2   7  3
      """
      level = kwargs.pop("level", None) or 0
      kwargs["inplace"] = inplace
      val = mars_eval(
          expr, resolvers=(df,), target=df, level=level + 1, **kwargs
      )
      if not inplace:
          return val
  
imports: import ast
  import binascii
  import operator
  import sys
  import tokenize
  import textwrap
  from collections import OrderedDict
  from functools import reduce
  from io import StringIO
  import numpy
  import pandas
  from ...None import opcodes
  from ...core import ENTITY_TYPE, OutputType, get_output_types, recursive_tile
  from ...serialization.serializables import BoolField, DictField, StringField
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import parse_index
label_desc: Use image node_id 3 for calling a global function with example usage: df_eval(df, expr, inplace) and returns: val