code: def _call_classification_model_on_standard_input(
      self, config, grad_key
  ):
      inputs = [
          {"segment": "a b c d"},
          {"segment": "a b c d"},
          {"segment": "e f e f"},
          {"segment": "e f e f"},
          {"segment": "e f e f"},
      ]
      model = testing_utils.ClassificationModelForTesting()
      dataset = lit_dataset.Dataset(None, None)
  
      model_outputs = [
          {
              grad_key: np.array(
                  [
                      [0, 0, 1, 1],
                      [0, 1, 0, 0],
                      [1, 1, 1, 1],
                      [1, 0, 1, 1],
                  ]
              ),
              "tokens": ["a", "b", "c", "d"],
              "grad_class": "1",
          },
          {
              grad_key: np.array(
                  [
                      [0, 0, 1, 1],
                      [0, 1, 0, 0],
                      [1, 1, 1, 1],
                      [1, 0, 1, 1],
                  ]
              ),
              "tokens": ["a", "b", "c", "d"],
              "grad_class": "1",
          },
          {
              grad_key: np.array(
                  [
                      [1, 1, 1, 1],
                      [1, 0, 1, 1],
                      [1, 1, 1, 1],
                      [0, 0, 1, 1],
                  ]
              ),
              "tokens": ["e", "f", "e", "g"],
              "grad_class": "1",
          },
          {
              grad_key: np.array(
                  [
                      [1, 1, 1, 1],
                      [1, 0, 1, 1],
                      [1, 1, 1, 1],
                      [0, 0, 1, 1],
                  ]
              ),
              "tokens": ["e", "f", "e", "g"],
              "grad_class": "1",
          },
          {
              grad_key: np.array(
                  [
                      [1, 1, 1, 1],
                      [1, 0, 1, 1],
                      [1, 1, 1, 1],
                      [0, 0, 1, 1],
                  ]
              ),
              "tokens": ["e", "f", "e", "g"],
              "grad_class": "1",
          },
      ]
  
      clustering_component = salience_clustering.SalienceClustering(
          self.salience_mappers
      )
      result = clustering_component.run(
          inputs, model, dataset, model_outputs, config
      )
      return (
          result,
          clustering_component,
          inputs,
          model,
          dataset,
          model_outputs,
      )
  
imports: from absl.testing import absltest
  from absl.testing import parameterized
  from lit_nlp.api import dataset
  from lit_nlp.api import dtypes
  from lit_nlp.components import gradient_maps
  from lit_nlp.components import lime_explainer
  from lit_nlp.components import salience_clustering
  from lit_nlp.lib import testing_utils
  import numpy
label_desc: Use image node_id 2 for calling the SalienceClusteringTest obj's underlying member method code with example usage: obj._call_classification_model_on_standard_input(config, grad_key) and returns: result,  clustering_component,  inputs,  model,  dataset,  model_outputs