code: def df_query(df, expr, inplace=False, **kwargs):
      """
      Query the columns of a DataFrame with a boolean expression.
  
      Parameters
      ----------
      expr : str
          The query string to evaluate.
  
          You can refer to variables
          in the environment by prefixing them with an '@' character like
          ``@a + b``.
  
          You can refer to column names that contain spaces or operators by
          surrounding them in backticks. This way you can also escape
          names that start with a digit, or those that  are a Python keyword.
          Basically when it is not valid Python identifier. See notes down
          for more details.
  
          For example, if one of your columns is called ``a a`` and you want
          to sum it with ``b``, your query should be ```a a` + b``.
  
      inplace : bool
          Whether the query should modify the data in place or return
          a modified copy.
      **kwargs
          See the documentation for :func:`eval` for complete details
          on the keyword arguments accepted by :meth:`DataFrame.query`.
  
      Returns
      -------
      DataFrame
          DataFrame resulting from the provided query expression.
  
      See Also
      --------
      eval : Evaluate a string describing operations on
          DataFrame columns.
      DataFrame.eval : Evaluate a string describing operations on
          DataFrame columns.
  
      Notes
      -----
      The result of the evaluation of this expression is first passed to
      :attr:`DataFrame.loc` and if that fails because of a
      multidimensional key (e.g., a DataFrame) then the result will be passed
      to :meth:`DataFrame.__getitem__`.
  
      This method uses the top-level :func:`eval` function to
      evaluate the passed query.
  
      The :meth:`~pandas.DataFrame.query` method uses a slightly
      modified Python syntax by default. For example, the ``&`` and ``|``
      (bitwise) operators have the precedence of their boolean cousins,
      :keyword:`and` and :keyword:`or`. This *is* syntactically valid Python,
      however the semantics are different.
  
      You can change the semantics of the expression by passing the keyword
      argument ``parser='python'``. This enforces the same semantics as
      evaluation in Python space. Likewise, you can pass ``engine='python'``
      to evaluate an expression using Python itself as a backend. This is not
      recommended as it is inefficient compared to using ``numexpr`` as the
      engine.
  
      The :attr:`DataFrame.index` and
      :attr:`DataFrame.columns` attributes of the
      :class:`~pandas.DataFrame` instance are placed in the query namespace
      by default, which allows you to treat both the index and columns of the
      frame as a column in the frame.
      The identifier ``index`` is used for the frame index; you can also
      use the name of the index to identify it in a query. Please note that
      Python keywords may not be used as identifiers.
  
      For further details and examples see the ``query`` documentation in
      :ref:`indexing <indexing.query>`.
  
      *Backtick quoted variables*
  
      Backtick quoted variables are parsed as literal Python code and
      are converted internally to a Python valid identifier.
      This can lead to the following problems.
  
      During parsing a number of disallowed characters inside the backtick
      quoted string are replaced by strings that are allowed as a Python identifier.
      These characters include all operators in Python, the space character, the
      question mark, the exclamation mark, the dollar sign, and the euro sign.
      For other characters that fall outside the ASCII range (U+0001..U+007F)
      and those that are not further specified in PEP 3131,
      the query parser will raise an error.
      This excludes whitespace different than the space character,
      but also the hashtag (as it is used for comments) and the backtick
      itself (backtick can also not be escaped).
  
      In a special case, quotes that make a pair around a backtick can
      confuse the parser.
      For example, ```it's` > `that's``` will raise an error,
      as it forms a quoted string (``'s > `that'``) with a backtick inside.
  
      See also the Python documentation about lexical analysis
      (https://docs.python.org/3/reference/lexical_analysis.html)
      in combination with the source code in :mod:`pandas.core.computation.parsing`.
  
      Examples
      --------
      >>> import mars.dataframe as md
      >>> df = md.DataFrame({'A': range(1, 6),
      ...                    'B': range(10, 0, -2),
      ...                    'C C': range(10, 5, -1)})
      >>> df.execute()
         A   B  C C
      0  1  10   10
      1  2   8    9
      2  3   6    8
      3  4   4    7
      4  5   2    6
      >>> df.query('A > B').execute()
         A  B  C C
      4  5  2    6
  
      The previous expression is equivalent to
  
      >>> df[df.A > df.B].execute()
         A  B  C C
      4  5  2    6
  
      For columns with spaces in their name, you can use backtick quoting.
  
      >>> df.query('B == `C C`').execute()
         A   B  C C
      0  1  10   10
  
      The previous expression is equivalent to
  
      >>> df[df.B == df['C C']].execute()
         A   B  C C
      0  1  10   10
      """
      level = kwargs.pop("level", None) or 0
      predicate = mars_eval(
          expr, resolvers=(df,), level=level + 1, **kwargs
      )
      result = df[predicate]
  
      if isinstance(predicate.op, DataFrameEval):
          output_type = get_output_types(result)[0]
          dtypes = (
              result.dtypes
              if result.ndim == 2
              else (result.name, result.dtype)
          )
          result = predicate.op.convert_to_query(
              df, output_type, result.shape, dtypes
          )
  
      if inplace:
          df.data = result.data
      else:
          return result
  
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 4 for calling a global function with example usage: df_query(df, expr, inplace) and returns: result