code: def run_compare_tf(
      graph,
      feed_dict,
      output_nodes,
      inputs_for_conversion=None,
      compute_unit=ct.ComputeUnit.CPU_ONLY,
      frontend_only=False,
      frontend="tensorflow",
      backend=("neuralnetwork", "fp32"),
      atol=1e-04,
      rtol=1e-05,
      freeze_graph=False,
      tf_outputs=None,
      minimum_deployment_target=None,
  ):
      if minimum_deployment_target is not None:
          validate_minimum_deployment_target(
              minimum_deployment_target, backend
          )
  
      res = run_compare_tf(
          graph,
          feed_dict,
          output_nodes,
          inputs_for_conversion=inputs_for_conversion,
          compute_unit=compute_unit,
          frontend_only=frontend_only,
          frontend=frontend,
          backend=backend,
          atol=atol,
          rtol=rtol,
          freeze_graph=freeze_graph,
          tf_outputs=tf_outputs,
          minimum_deployment_target=minimum_deployment_target,
      )
  
      alist = []
      if res is not None:
          alist = list(res)
      alist.append(TensorFlowBaseTest.testclassname)
      alist.append(TensorFlowBaseTest.testmodelname)
  
      return tuple(alist)
  
imports: import os
  import tempfile
  import numpy
  import pytest
  import coremltools.models.utils
  from coremltools._deps import _HAS_TF_2
  from coremltools.converters.mil.testing_reqs import ct
  from coremltools.converters.mil.testing_utils import compare_backend, ct_convert, validate_minimum_deployment_target
  from tensorflow.python.framework import dtypes
  from tensorflow.python.keras.saving import saving_utils
  from tensorflow.python.tools.freeze_graph import freeze_graph
label_desc: Use image node_id 2 for calling the TensorFlowBaseTest obj's underlying member method code with example usage: obj.run_compare_tf(graph, feed_dict, output_nodes, inputs_for_conversion, compute_unit, frontend_only, frontend, backend, atol, rtol, freeze_graph, tf_outputs, minimum_deployment_target) and returns: tuple