code: def autoformat_stride_3d(strides):
      if isinstance(strides, int):
          return [1, strides, strides, strides, 1]
      elif isinstance(strides, (tuple, list, tf.TensorShape)):
          if len(strides) == 3:
              return [1, strides[0], strides[1], strides[2], 1]
          elif len(strides) == 5:
              assert (
                  strides[0] == strides[4] == 1
              ), "Must have strides[0] = strides[4] = 1"
              return [
                  strides[0],
                  strides[1],
                  strides[2],
                  strides[3],
                  strides[4],
              ]
          else:
              raise Exception(
                  "strides length error: "
                  + str(len(strides))
                  + ", only a length of 3 or 5 is supported."
              )
      else:
          raise Exception("strides format error: " + str(type(strides)))
  
imports: from __future__ import division, print_function, absolute_import
  import six
  import string
  import random
  import numpy
  import tensorflow.compat.v1
  from tensorflow.python import pywrap_tensorflow
  import tflearn.variables
label_desc: Use image node_id 30 for calling a global function with example usage: autoformat_stride_3d(strides) and returns: list, list, list