code: def relax_input_shape(shape_1, shape_2):
      if shape_1 is None or shape_2 is None:
          return None
      if len(shape_1) != len(shape_2):
          return None
      return tuple(
          None if d1 != d2 else d1 for d1, d2 in zip(shape_1, shape_2)
      )
  
imports: import copy
  import warnings
  from tensorflow.python import tf2
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.keras import layers
  from tensorflow.python.keras.engine import base_layer
  from tensorflow.python.keras.engine import functional
  from tensorflow.python.keras.engine import input_layer
  from tensorflow.python.keras.engine import training_utils
  from tensorflow.python.keras.saving.saved_model import model_serialization
  from tensorflow.python.keras.utils import generic_utils
  from tensorflow.python.keras.utils import layer_utils
  from tensorflow.python.keras.utils import tf_inspect
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.module import module
  from tensorflow.python.ops.numpy_ops import np_arrays
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.trackable import base
  from tensorflow.python.util import nest
label_desc: Use image node_id 2 for calling a global function with example usage: relax_input_shape(shape_1, shape_2) and returns: tuple, None, None