code: def shares_memory(a, b, max_work=None):
      """
      shares_memory(a, b, /, max_work=None)
  
      Determine if two arrays share memory.
  
      .. warning::
  
         This function can be exponentially slow for some inputs, unless
         `max_work` is set to a finite number or ``MAY_SHARE_BOUNDS``.
         If in doubt, use `numpy.may_share_memory` instead.
  
      Parameters
      ----------
      a, b : ndarray
          Input arrays
      max_work : int, optional
          Effort to spend on solving the overlap problem (maximum number
          of candidate solutions to consider). The following special
          values are recognized:
  
          max_work=MAY_SHARE_EXACT  (default)
              The problem is solved exactly. In this case, the function returns
              True only if there is an element shared between the arrays. Finding
              the exact solution may take extremely long in some cases.
          max_work=MAY_SHARE_BOUNDS
              Only the memory bounds of a and b are checked.
  
      Raises
      ------
      numpy.exceptions.TooHardError
          Exceeded max_work.
  
      Returns
      -------
      out : bool
  
      See Also
      --------
      may_share_memory
  
      Examples
      --------
      >>> x = np.array([1, 2, 3, 4])
      >>> np.shares_memory(x, np.array([5, 6, 7]))
      False
      >>> np.shares_memory(x[::2], x)
      True
      >>> np.shares_memory(x[::2], x[1::2])
      False
  
      Checking whether two arrays share memory is NP-complete, and
      runtime may increase exponentially in the number of
      dimensions. Hence, `max_work` should generally be set to a finite
      number, as it is possible to construct examples that take
      extremely long to run:
  
      >>> from numpy.lib.stride_tricks import as_strided
      >>> x = np.zeros([192163377], dtype=np.int8)
      >>> x1 = as_strided(
      ...     x, strides=(36674, 61119, 85569), shape=(1049, 1049, 1049))
      >>> x2 = as_strided(
      ...     x[64023025:], strides=(12223, 12224, 1), shape=(1049, 1049, 1))
      >>> np.shares_memory(x1, x2, max_work=1000)
      Traceback (most recent call last):
      ...
      numpy.exceptions.TooHardError: Exceeded max_work
  
      Running ``np.shares_memory(x1, x2)`` without `max_work` set takes
      around 1 minute for this case. It is possible to find problems
      that take still significantly longer.
  
      """
      return (a, b)
  
imports: import functools
  from .None import overrides
  from .None import _multiarray_umath
  from ._multiarray_umath import *
  from ._multiarray_umath import _flagdict, from_dlpack, _place, _reconstruct, _vec_string, _ARRAY_API, _monotonicity, _get_ndarray_c_version, _get_madvise_hugepage, _set_madvise_hugepage, _get_promotion_state, _set_promotion_state
label_desc: Use image node_id 18 for calling a global function with example usage: shares_memory(a, b, max_work) and returns: a,  b