class_docstr: TensorFlow Keras model for penguin classification.
code: def __init__(self, path: str):
      path = file_cache.cached_path(path)
      self.model = tf.keras.models.load_model(path)
      # Feature column means and variance to normalize values before
      # prediction.
      self.means = np.array(
          [
              4.23795547e03,
              1.71222672e01,
              4.40004048e01,
              2.01587045e02,
              5.14170040e-01,
              3.40080972e-01,
              1.45748988e-01,
              4.89878543e-01,
              5.10121457e-01,
          ]
      )
      self.vars = np.array(
          [
              6.29523451e05,
              4.17662973e00,
              2.76167220e01,
              1.97108820e02,
              2.49799210e-01,
              2.24425904e-01,
              1.24506220e-01,
              2.49897556e-01,
              2.49897556e-01,
          ]
      )
  
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 1 to create a new PenguinModel object from inherited base classes: lit_model with example: obj = PenguinModel(path)