parameters: (a, b, max_work=None)
return_statement: return _api_internal.share_memory(a, b).item()
docstring: Determine if two arrays might share memory
  
      A return of True does not necessarily mean that the two arrays
      share any element.  It just means that they *might*.
  
      Only the memory bounds of a and b are checked by default.
  
      Parameters
      ----------
      a, b : ndarray
          Input arrays
  
      Returns
      -------
      out : bool
  
      See Also
      --------
      shares_memory
  
      Examples
      --------
      >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
      False
      >>> x = np.zeros([3, 4])
      >>> np.may_share_memory(x[:,0], x[:,1])
      True
  
      This function differs from the original `numpy.may_share_memory
      <https://docs.scipy.org/doc/numpy/reference/generated/numpy.may_share_memory.html>`_ in
      the following way(s):
  
      - Does not support `max_work`, it is a dummy argument
      - Actually it is same as `shares_memory` in MXNet np
docstring_summary: Determine if two arrays might share memory
docstring_tokens: Determine if two arrays might share memory
function: def may_share_memory(a, b, max_work=None):
      """
      Determine if two arrays might share memory
  
      A return of True does not necessarily mean that the two arrays
      share any element.  It just means that they *might*.
  
      Only the memory bounds of a and b are checked by default.
  
      Parameters
      ----------
      a, b : ndarray
          Input arrays
  
      Returns
      -------
      out : bool
  
      See Also
      --------
      shares_memory
  
      Examples
      --------
      >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
      False
      >>> x = np.zeros([3, 4])
      >>> np.may_share_memory(x[:,0], x[:,1])
      True
  
      This function differs from the original `numpy.may_share_memory
      <https://docs.scipy.org/doc/numpy/reference/generated/numpy.may_share_memory.html>`_ in
      the following way(s):
  
      - Does not support `max_work`, it is a dummy argument
      - Actually it is same as `shares_memory` in MXNet np
      """
      return _api_internal.share_memory(a, b).item()
function_tokens: def may_share_memory ( a , b , max_work = None ) : return _api_internal . share_memory ( a , b ) . item ( )