code: def test_tf2keras_incorrect_range_dim(backend):
      input_dim = 3
      # None denotes seq_len dimension
      x1 = tf.keras.Input(shape=(None, input_dim), name="seq1")
      y = x1 + 1
      keras_model = tf.keras.Model(inputs=[x1], outputs=[y])
  
      # Incorrectly using -1 instead of ct.RangeDim
      # One RangeDim shared by two inputs
      with pytest.raises(
          ValueError, match=r"Can\'t convert to CoreML shaping"
      ):
          seq1_input = ct.TensorType(
              name="seq1", shape=(1, -1, input_dim)
          )
          mlmodel = ct.convert(
              keras_model, inputs=[seq1_input], convert_to=backend[0]
          )
  
imports: import itertools
  import os
  import platform
  from os import chdir, getcwd
  from shutil import rmtree
  from tempfile import mkdtemp
  import numpy
  import pytest
  import coremltools
  from coremltools.converters.mil.frontend.tensorflow.test.test_tf_conversion_api import TestInputOutputConversionAPI
  from coremltools.converters.mil.frontend.tensorflow.test.test_tf_conversion_api import TestiOS16DefaultIODtype
  from coremltools.converters.mil.mil import types
  from coremltools.converters.mil.testing_reqs import backends
  import tensorflow
  from tensorflow import keras
  from tensorflow.keras import layers
label_desc: Use image node_id 2 for calling the TestTF2FlexibleInput obj's underlying member method code with example usage: obj.test_tf2keras_incorrect_range_dim(backend) without return types