code: def comparison_op(left: ArrayLike, right: Any, op) -> ArrayLike:
      """
      Evaluate a comparison operation `=`, `!=`, `>=`, `>`, `<=`, or `<`.
  
      Note: the caller is responsible for ensuring that numpy warnings are
      suppressed (with np.errstate(all="ignore")) if needed.
  
      Parameters
      ----------
      left : np.ndarray or ExtensionArray
      right : object
          Cannot be a DataFrame, Series, or Index.
      op : {operator.eq, operator.ne, operator.gt, operator.ge, operator.lt, operator.le}
  
      Returns
      -------
      ndarray or ExtensionArray
      """
      # NB: We assume extract_array has already been called on left and right
      lvalues = ensure_wrapped_if_datetimelike(left)
      rvalues = ensure_wrapped_if_datetimelike(right)
  
      rvalues = lib.item_from_zerodim(rvalues)
      if isinstance(rvalues, list):
          # We don't catch tuple here bc we may be comparing e.g. MultiIndex
          #  to a tuple that represents a single entry, see test_compare_tuple_strs
          rvalues = np.asarray(rvalues)
  
      if isinstance(rvalues, (np.ndarray, ABCExtensionArray)):
          # TODO: make this treatment consistent across ops and classes.
          #  We are not catching all listlikes here (e.g. frozenset, tuple)
          #  The ambiguous case is object-dtype.  See GH#27803
          if len(lvalues) != len(rvalues):
              raise ValueError(
                  "Lengths must match to compare",
                  lvalues.shape,
                  rvalues.shape,
              )
  
      if should_extension_dispatch(lvalues, rvalues) or (
          (
              isinstance(rvalues, (Timedelta, BaseOffset, Timestamp))
              or right is NaT
          )
          and lvalues.dtype != object
      ):
          # Call the method on lvalues
          res_values = op(lvalues, rvalues)
  
      elif is_scalar(rvalues) and isna(rvalues):  # TODO: but not pd.NA?
          # numpy does not like comparisons vs None
          if op is operator.ne:
              res_values = np.ones(lvalues.shape, dtype=bool)
          else:
              res_values = np.zeros(lvalues.shape, dtype=bool)
  
      elif is_numeric_v_string_like(lvalues, rvalues):
          # GH#36377 going through the numexpr path would incorrectly raise
          return invalid_comparison(lvalues, rvalues, op)
  
      elif lvalues.dtype == object or isinstance(rvalues, str):
          res_values = comp_method_OBJECT_ARRAY(op, lvalues, rvalues)
  
      else:
          res_values = _na_arithmetic_op(
              lvalues, rvalues, op, is_cmp=True
          )
  
      return res_values
  
imports: from __future__ import annotations
  import datetime
  from functools import partial
  import operator
  from typing import TYPE_CHECKING, Any
  import warnings
  import numpy
  from pandas._libs import NaT, Timedelta, Timestamp, lib, ops
  from pandas._libs.tslibs import BaseOffset, get_supported_dtype, is_supported_dtype, is_unitless
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike, find_common_type
  from pandas.core.dtypes.common import ensure_object, is_bool_dtype, is_list_like, is_numeric_v_string_like, is_object_dtype, is_scalar
  from pandas.core.dtypes.generic import ABCExtensionArray, ABCIndex, ABCSeries
  from pandas.core.dtypes.missing import isna, notna
  from pandas.core import roperator
  from pandas.core.computation import expressions
  from pandas.core.construction import ensure_wrapped_if_datetimelike
  from pandas.core.ops import missing
  from pandas.core.ops.dispatch import should_extension_dispatch
  from pandas.core.ops.invalid import invalid_comparison
label_desc: Use image node_id 6 for calling a global function with example usage: comparison_op(left, right, op) and returns: res_values, invalid_comparison