code: def _inputs_swap_needed(mode, shape1, shape2):
      """
      If in 'valid' mode, returns whether or not the input arrays need to be
      swapped depending on whether `shape1` is at least as large as `shape2` in
      every dimension.
  
      This is important for some of the correlation and convolution
      implementations in this module, where the larger array input needs to come
      before the smaller array input when operating in this mode.
  
      Note that if the mode provided is not 'valid', False is immediately
      returned.
      """
      if mode == "valid":
          ok1, ok2 = True, True
  
          for d1, d2 in zip(shape1, shape2):
              if not d1 >= d2:
                  ok1 = False
              if not d2 >= d1:
                  ok2 = False
  
          if not (ok1 or ok2):
              raise ValueError(
                  "For 'valid' mode, one must be at least "
                  "as large as the other in every dimension"
              )
  
          return not ok1
  
      return False
  
file_path: cusignal/python/cusignal/convolution/convolution_utils.py
imports: import timeit
  import cupy
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: _inputs_swap_needed(mode, shape1, shape2) and returns: False, unknown