code: def test_multiarray_input_rangedim(self, backend, compute_unit):
      convert_to = backend[0]
      if convert_to == "mlprogram" and ct.utils._macos_version() < (
          12,
          0,
      ):
          return
  
      example_input = torch.rand(1, 3, 50, 50) * 100
      traced_model = torch.jit.trace(
          TestConvModule().eval(), example_input
      )
  
      input_shape = ct.Shape(
          shape=(
              1,
              3,
              ct.RangeDim(25, 100, default=45),
              ct.RangeDim(25, 100, default=45),
          )
      )
      model = ct.convert(
          traced_model,
          inputs=[ct.TensorType(shape=input_shape)],
          convert_to=convert_to,
          compute_units=compute_unit,
      )
  
      spec = model.get_spec()
      assert list(
          spec.description.input[0].type.multiArrayType.shape
      ) == [1, 3, 45, 45]
      assert (
          spec.description.input[0]
          .type.multiArrayType.shapeRange.sizeRanges[2]
          .lowerBound
          == 25
      )
      assert (
          spec.description.input[0]
          .type.multiArrayType.shapeRange.sizeRanges[2]
          .upperBound
          == 100
      )
      _assert_torch_coreml_output_shapes(
          model, spec, traced_model, example_input
      )
  
imports: import itertools
  import os
  import tempfile
  import numpy
  import PIL.Image
  import pytest
  import coremltools
  from coremltools._deps import _HAS_TF_2, _HAS_TORCH, MSG_TF2_NOT_FOUND, MSG_TORCH_NOT_FOUND
  from coremltools.converters.mil import Builder
  from coremltools.converters.mil.testing_reqs import backends, compute_units
label_desc: Use image node_id 1 for calling the TestFlexibleInputShapesTorch obj's underlying member method code with example usage: obj.test_multiarray_input_rangedim(backend, compute_unit) without return types