class_docstr: A test model for the interpreter that uses 'TEST_DATA' as model output.
code: def predict_minibatch(
      self, inputs: list[lit_types.JsonDict], **unused
  ) -> list[lit_types.JsonDict]:
      output = []
  
      def predict_example(
          ex: lit_types.JsonDict,
      ) -> tuple[float, float, float]:
          x = ex["x"]
          return TEST_DATA[x].prediction
  
      for example in inputs:
          output.append(
              {
                  "pred": predict_example(example),
                  "aux_pred": [1 / 3, 1 / 3, 1 / 3],
              }
          )
      return output
  
imports: from typing import NamedTuple
  from absl.testing import absltest
  from absl.testing import parameterized
  from lit_nlp.api import dataset
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.components import curves
  from lit_nlp.lib import caching
label_desc: Use image node_id 3 for calling the _StaticTestModel obj's underlying member method code with example usage: obj.predict_minibatch(inputs) and returns: output, TEST_DATA