class_docstr: TensorFlow Keras model for penguin classification.
code: def predict_minibatch(self, inputs):
      def convert_input(inp):
          ex = np.array(
              [
                  inp["body_mass_g"],
                  inp["culmen_depth_mm"],
                  inp["culmen_length_mm"],
                  inp["flipper_length_mm"],
                  0,
                  0,
                  0,
                  0,
                  0,
              ]
          )
          # Set one-hot encodings of categorical features.
          island_index = _VOCABS["island"].index(inp["island"])
          sex_index = _VOCABS["sex"].index(inp["sex"])
          # Island one-hot encodings start at input index 4.
          ex[island_index + 4] = 1
          # Sex one-hot encodings start at input index 7.
          ex[sex_index + 7] = 1
  
          # Normalize feature values.
          ex = (ex - self.means) / (self.vars**0.5)
          return ex.tolist()
  
      adjusted_inputs = [convert_input(inp) for inp in inputs]
      model_output = self.model.predict(adjusted_inputs)
  
      ret = [{"predicted_species": out} for out in model_output]
      return ret
  
file_path: lit/lit_nlp/examples/models/penguin_model.py
imports: from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.examples.datasets import penguin_data
  from lit_nlp.lib import file_cache
  import numpy
  import tensorflow
label_desc: Use image node_id 3 for calling the PenguinModel obj's underlying member method code with example usage: obj.predict_minibatch(inputs) and returns: ret, ex