code: def mars_eval(
      expr,
      parser="mars",
      engine=None,
      local_dict=None,
      global_dict=None,
      resolvers=(),
      level=0,
      target=None,
      inplace=False,
  ):
      """
  
      Evaluate a Python expression as a string using various backends.
  
      The following arithmetic operations are supported: ``+``, ``-``, ``*``,
      ``/``, ``**``, ``%``, ``//`` (python engine only) along with the following
      boolean operations: ``|`` (or), ``&`` (and), and ``~`` (not).
      Additionally, the ``'pandas'`` parser allows the use of :keyword:`and`,
      :keyword:`or`, and :keyword:`not` with the same semantics as the
      corresponding bitwise operators.  :class:`~pandas.Series` and
      :class:`~pandas.DataFrame` objects are supported and behave as they would
      with plain ol' Python evaluation.
  
      Parameters
      ----------
      expr : str
          The expression to evaluate. This string cannot contain any Python
          `statements
          <https://docs.python.org/3/reference/simple_stmts.html#simple-statements>`__,
          only Python `expressions
          <https://docs.python.org/3/reference/simple_stmts.html#expression-statements>`__.
      local_dict : dict or None, optional
          A dictionary of local variables, taken from locals() by default.
      global_dict : dict or None, optional
          A dictionary of global variables, taken from globals() by default.
      resolvers : list of dict-like or None, optional
          A list of objects implementing the ``__getitem__`` special method that
          you can use to inject an additional collection of namespaces to use for
          variable lookup. For example, this is used in the
          :meth:`~DataFrame.query` method to inject the
          ``DataFrame.index`` and ``DataFrame.columns``
          variables that refer to their respective :class:`~pandas.DataFrame`
          instance attributes.
      level : int, optional
          The number of prior stack frames to traverse and add to the current
          scope. Most users will **not** need to change this parameter.
      target : object, optional, default None
          This is the target object for assignment. It is used when there is
          variable assignment in the expression. If so, then `target` must
          support item assignment with string keys, and if a copy is being
          returned, it must also support `.copy()`.
      inplace : bool, default False
          If `target` is provided, and the expression mutates `target`, whether
          to modify `target` inplace. Otherwise, return a copy of `target` with
          the mutation.
  
      Returns
      -------
      ndarray, numeric scalar, DataFrame, Series
  
      Raises
      ------
      ValueError
          There are many instances where such an error can be raised:
  
          - `target=None`, but the expression is multiline.
          - The expression is multiline, but not all them have item assignment.
            An example of such an arrangement is this:
  
            a = b + 1
            a + 2
  
            Here, there are expressions on different lines, making it multiline,
            but the last line has no variable assigned to the output of `a + 2`.
          - `inplace=True`, but the expression is missing item assignment.
          - Item assignment is provided, but the `target` does not support
            string item assignment.
          - Item assignment is provided and `inplace=False`, but the `target`
            does not support the `.copy()` method
  
      See Also
      --------
      DataFrame.query : Evaluates a boolean expression to query the columns
              of a frame.
      DataFrame.eval : Evaluate a string describing operations on
              DataFrame columns.
  
      Notes
      -----
      The ``dtype`` of any objects involved in an arithmetic ``%`` operation are
      recursively cast to ``float64``.
  
      See the :ref:`enhancing performance <enhancingperf.eval>` documentation for
      more details.
  
      Examples
      --------
      >>> import mars.dataframe as md
      >>> df = md.DataFrame({"animal": ["dog", "pig"], "age": [10, 20]})
      >>> df.execute()
        animal  age
      0    dog   10
      1    pig   20
  
      We can add a new column using ``pd.eval``:
  
      >>> md.eval("double_age = df.age * 2", target=df).execute()
        animal  age  double_age
      0    dog   10          20
      1    pig   20          40
      """
      if not isinstance(expr, str):
          raise TypeError("expr must be a string")
  
      expr = textwrap.dedent(expr)
  
      try:
          frame = sys._getframe(level + 1)
          local_dict = local_dict or dict()
          local_dict.update(frame.f_locals)
          global_dict = global_dict or dict()
          global_dict.update(frame.f_globals)
      finally:
          del frame
  
      env = dict()
      env.update(global_dict)
      env.update(local_dict)
  
      ref_frames = set(resolvers) | set(
          [target] if target is not None else []
      )
      self_target = len(resolvers) > 0 and resolvers[0] is target
  
      if target is not None and not inplace:
          target = target.copy()
  
      visitor = CollectionVisitor(resolvers, target, env)
      result = visitor.eval(expr)
      result = result if result is not None else target
      has_var_frame = any(
          isinstance(env[k], ENTITY_TYPE)
          for k in visitor.referenced_vars
      )
      if (
          len(ref_frames) != 1
          or visitor.entity_subscribe
          or has_var_frame
      ):
          if parser != "mars":
              raise NotImplementedError(
                  "Does not support parser names other than mars"
              )
          if engine is not None:
              raise NotImplementedError(
                  "Does not support specifying engine names"
              )
          return result
      else:
          parser = "pandas" if parser == "mars" else parser
          referenced_env = {k: env[k] for k in visitor.referenced_vars}
          op = DataFrameEval(
              expr,
              parser=parser,
              engine=engine,
              variables=referenced_env,
              self_target=visitor.assigned and self_target,
              is_query=False,
          )
          output_type = get_output_types(result)[0]
          dtypes = (
              result.dtypes
              if result.ndim == 2
              else (result.name, result.dtype)
          )
          return op(resolvers[0], output_type, result.shape, dtypes)
  
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 2 for calling a global function with example usage: mars_eval(expr, parser, engine, local_dict, global_dict, resolvers, level, target, inplace) and returns: result, op