class_docstr: A test model for testing the regression case.
class_docstr_tok: ["A","test","model","for","testing","the","regression","case","."]
docstr: Simulates regression of x1 + 2 * x2 using elements of the image array.
docstr_tok: ["Simulates","regression","of","x1","+","2","*","x2","using","elements","of","the","image","array","."]
code: def predict_minibatch(self, inputs: list[JsonDict]) -> list[JsonDict]:
      """Simulates regression of x1 + 2 * x2 using elements of the image array."""
      result = []
      for example in inputs:
          img = example["image"]
          if isinstance(img, str):
              img = image_utils.convert_image_str_to_array(
                  img, shape=self.GRADIENT_SHAPE
              )
          pred = img[0, 0, 0] + img[1, 0, 0] * 2
          grad = np.zeros(shape=self.GRADIENT_SHAPE)
          grad[0, 0, 0] = 1
          grad[1, 0, 0] = 2
          result.append({"pred": pred, "grads": grad})
      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 RegressionTestModel obj's underlying member method code with example usage: obj.predict_minibatch(inputs) and returns: result