code: def run_compare_torch(
      input_data,
      model,
      atol=1e-04,
      rtol=1e-05,
      input_as_shape=True,
      minimum_deployment_target=ct.target.iOS17,
  ):
      # TODO(rdar://108472419): properly design a random input
      if input_as_shape:
          input_data = [torch.ones(*shape) for shape in input_data]
  
      return TorchBaseTest.run_compare_torch(
          input_data,
          model,
          atol=atol,
          rtol=rtol,
          input_as_shape=False,
          backend=("mlprogram", "fp32"),
          use_scripting=False,
          compute_unit=ct.ComputeUnit.CPU_ONLY,
          minimum_deployment_target=minimum_deployment_target,
      )
  
imports: import itertools
  from typing import Optional
  import numpy
  import pytest
  import torch
  import torchvision
  import coremltools
  from coremltools import TensorType
  from coremltools._deps import _HAS_TORCH, _HAS_TORCH_VISION, MSG_TORCH_NOT_FOUND, MSG_TORCH_VISION_NOT_FOUND
  from coremltools.converters.mil.testing_utils import get_op_types_in_program
  from .testing_utils import TorchBaseTest
label_desc: Use image node_id 1 for calling the TorchQuantizationBaseTest obj's underlying member method code with example usage: obj.run_compare_torch(input_data, model, atol, rtol, input_as_shape, minimum_deployment_target) and returns: TorchBaseTest