class_docstr: Implements lit.Model interface for testing classification models.
  
  Returns the same output for every input.
class_docstr_tok: ["Implements","lit.Model","interface","for","testing","classification","models",".","Returns","the","same","output","for","every","input","."]
code: def predict_minibatch(self, inputs: list[JsonDict], **kw):
      output = {
          "probas": np.array([0.2, 0.8]),
          "input_embs": np.array(
              [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
          ),
          "input_embs_grad": np.array(
              [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
          ),
          "tokens": ["test"],
          "grad_class": "1",
      }
      return map(lambda x: output, inputs)
  
imports: from collections.abc import Iterable, Iterator
  from lit_nlp.api import model
  from lit_nlp.api import types
  import numpy
  import numpy.testing
label_desc: Use image node_id 3 for calling the ClassificationModelForTesting obj's underlying member method code with example usage: obj.predict_minibatch(inputs) and returns: map