code: def correlate(a, v, mode=None, *, old_behavior=False):
      dtypes = [x.dtype for x in [a, v]]
      mode = mode if mode is not None else "valid"
      ivy.utils.assertions.check_equal(a.ndim, 1, as_array=False)
      ivy.utils.assertions.check_equal(v.ndim, 1, as_array=False)
      n = min(a.shape[0], v.shape[0])
      m = max(a.shape[0], v.shape[0])
      if a.shape[0] >= v.shape[0]:
          if mode == "full":
              r = n + m - 1
              for j in range(0, n - 1):
                  a = ivy.concat((ivy.array([0]), a), axis=0)
          elif mode == "same":
              r = m
              right_pad = (n - 1) // 2
              left_pad = (n - 1) - (n - 1) // 2
              for _ in range(0, left_pad):
                  a = ivy.concat((ivy.array([0]), a), axis=0)
              for _ in range(0, right_pad):
                  a = ivy.concat((a, ivy.array([0])), axis=0)
          elif mode == "valid":
              r = m - n + 1
          else:
              raise ivy.utils.exceptions.IvyException("invalid mode")
          ret = ivy.array(
              [
                  ivy.to_list((v[:n] * ivy.roll(a, -t)[:n]).sum())
                  for t in range(0, r)
              ],
              dtype=max(dtypes),
          )
      else:
          if mode == "full":
              r = n + m - 1
              for j in range(0, n - 1):
                  v = ivy.concat((ivy.array([0]), v), axis=0)
          elif mode == "same":
              r = m
              right_pad = (n - 1) // 2
              left_pad = (n - 1) - (n - 1) // 2
              for _ in range(0, left_pad):
                  v = ivy.concat((ivy.array([0]), v), axis=0)
              for _ in range(0, right_pad):
                  v = ivy.concat((v, ivy.array([0])), axis=0)
          elif mode == "valid":
              r = m - n + 1
          else:
              raise ivy.utils.exceptions.IvyException("invalid mode")
          ret = ivy.flip(
              ivy.array(
                  [
                      ivy.to_list((a[:n] * ivy.roll(v, -t)[:n]).sum())
                      for t in range(0, r)
                  ],
                  dtype=max(dtypes),
              )
          )
      return ret
  
label_desc: Use image node_id 2 for calling a global function with example usage: correlate(a, v, mode) and returns: ret