class_docstr: Groupwise RankingEstimator tests.
docstr: Initialize the model checkpoint with constant values.
docstr_tok: ["Initialize","the","model","checkpoint","with","constant","values","."]
code: def _initialize_checkpoint(self):
      """Initialize the model checkpoint with constant values."""
      with tf.Graph().as_default():
          tf.random.set_seed(23)
          tf.Variable(
              [[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]], name=WEIGHTS_NAME
          )
          tf.Variable([1.0, 1.0], name=BIAS_NAME)
          tf.Variable(
              100,
              name=tf.compat.v1.GraphKeys.GLOBAL_STEP,
              dtype=tf.int64,
          )
          # Adagrad weights should not be zero, otherwise we get NaNs.
          tf.Variable(
              [[1e-12, 1e-12], [1e-12, 1e-12], [1e-12, 1e-12]],
              name=ADAGRAD_WEIGHTS_NAME,
          )
          tf.Variable([1e-12, 1e-12], name=ADAGRAD_BIAS_NAME)
  
          _save_variables_to_ckpt(self._model_dir)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from absl.testing import parameterized
  import tensorflow
  from tensorflow import estimator
  from tensorflow_ranking.python import feature
  from tensorflow_ranking.python import head
  from tensorflow_ranking.python import losses
  from tensorflow_ranking.python import model
label_desc: Use image node_id 4 for calling the GroupwiseRankingEstimatorTest obj's underlying member method code with example usage: obj._initialize_checkpoint() without return types