code: def predict_minibatch(self, inputs: list[JsonDict]) -> list[JsonDict]:
      result = []
      for i, _ in enumerate(inputs):
          result.append(
              {
                  "preds": np.random.rand(len(self.LABELS)),
                  "grads": np.random.rand(*self.GRADIENT_SHAPE),
                  "grad_target": self.LABELS[i],
              }
          )
      return result
  
imports: from absl.testing import absltest
  from lit_nlp.api import dataset
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.components import image_gradient_maps
  from lit_nlp.lib import image_utils
  import numpy
  from PIL import Image
label_desc: Use image node_id 2 for calling the ClassificationTestModel obj's underlying member method code with example usage: obj.predict_minibatch(inputs) and returns: result