code: def setUpClass(
      self,
      model="resnet-50",
      feature="awesome_image",
      input_image_shape=(3, 224, 224),
      tol=0.02,
      num_examples=100,
      label_type=int,
  ):
      self.feature = feature
      self.target = "awesome_label"
      self.input_image_shape = input_image_shape
      self.pre_trained_model = model
      self.tolerance = tol
  
      # Get deep features if needed
      if self.feature.endswith("WithDeepFeature"):
          data[self.feature] = get_deep_features(
              data["awesome_image"],
              self.feature.split("_WithDeepFeature")[0],
          )
  
      self.model = tc.image_classifier.create(
          data,
          target=self.target,
          feature=self.feature,
          model=self.pre_trained_model,
          seed=42,
      )
      self.nn_model = self.model.feature_extractor
      self.lm_model = self.model.classifier
      self.max_iterations = 10
  
      self.get_ans = {
          "classifier": lambda x: type(x)
          == tc.logistic_classifier.LogisticClassifier,
          "feature": lambda x: x == self.feature,
          "classes": lambda x: x == self.lm_model.classes,
          "training_time": lambda x: x > 0,
          "input_image_shape": lambda x: x == self.input_image_shape,
          "target": lambda x: x == self.target,
          "feature_extractor": lambda x: callable(x.extract_features),
          "training_loss": lambda x: x > 0,
          "max_iterations": lambda x: x == self.max_iterations,
          "num_features": lambda x: x == self.lm_model.num_features,
          "num_examples": lambda x: x == self.lm_model.num_examples,
          "model": lambda x: (
              x == self.pre_trained_model
              or (
                  self.pre_trained_model == "VisionFeaturePrint_Screen"
                  and x == "VisionFeaturePrint_Scene"
              )
          ),
          "num_classes": lambda x: x == self.lm_model.num_classes,
      }
      self.fields_ans = self.get_ans.keys()
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import os
  import platform
  import sys
  import tempfile
  import unittest
  from turicreate.toolkits._main import ToolkitError
  from turicreate.toolkits._internal_utils import _mac_ver, _raise_error_if_not_sframe, _raise_error_if_not_sarray
  from turicreate.toolkits.image_analysis.image_analysis import MODEL_TO_FEATURE_SIZE_MAPPING, get_deep_features
  from .None import util
  import numpy
  import turicreate
label_desc: Use image node_id 1 for calling the ImageClassifierTest obj's underlying member method code with example usage: obj.setUpClass(model, feature, input_image_shape, tol, num_examples, label_type) without return types