code: def get_variable_initializer(hparams):
    
    if not hparams.initializer:
      return None
  
    mlperf_log.transformer_print(key=mlperf_log.MODEL_HP_INITIALIZER_GAIN,
                                 value=hparams.initializer_gain,
                                 hparams=hparams)
  
    if not tf.executing_eagerly():
      tf.logging.info("Using variable initializer: %s", hparams.initializer)
    if hparams.initializer == "orthogonal":
      return tf.orthogonal_initializer(gain=hparams.initializer_gain)
    elif hparams.initializer == "uniform":
      max_val = 0.1 * hparams.initializer_gain
      return tf.random_uniform_initializer(-max_val, max_val)
    elif hparams.initializer == "normal_unit_scaling":
      return tf.variance_scaling_initializer(
          hparams.initializer_gain, mode="fan_avg", distribution="normal")
    elif hparams.initializer == "uniform_unit_scaling":
      return tf.variance_scaling_initializer(
          hparams.initializer_gain, mode="fan_avg", distribution="uniform")
    elif hparams.initializer == "xavier":
      return tf.initializers.glorot_uniform()
    else:
      raise ValueError("Unrecognized initializer: %s" % hparams.initializer)
docs: Get variable initializer from hparams.