def __init__(self, role, bridge, example_ids, exporting=False, config_run=True, bias_tensor=None, vec_tensor=None, bias_embedding=None, vec_embedding=None, feature_columns=None):
    super(SparseFLModel, self).__init__(role, bridge, example_ids, exporting)
    self._config_run = config_run
    self._num_shards = 1
    if config_run:
        self._bias_tensor = tf.placeholder(tf.float32, shape=[None, None])
        self._vec_tensor = tf.placeholder(tf.float32, shape=[None, None])
    else:
        self._bias_tensor = bias_tensor
        self._vec_tensor = vec_tensor
    self._bias_embedding = bias_embedding
    self._vec_embedding = vec_embedding
    self._feature_columns = feature_columns
    self._frozen = False
    self._slot_ids = []
    self._feature_slots = {}
    self._feature_column_v1s = {}
    self._use_fid_v2 = False
    self._num_embedding_groups = 3
def __init__(self, role, bridge, example_ids, exporting=False, config_run=True, bias_tensor=None, vec_tensor=None, bias_embedding=None, vec_embedding=None, feature_columns=None):
    super(SparseFLModel, self).__init__(role, bridge, example_ids, exporting)
    self._config_run = config_run
    self._num_shards = 1
    if config_run:
        self._bias_tensor = tf.placeholder(tf.float32, shape=[None, None])
        self._vec_tensor = tf.placeholder(tf.float32, shape=[None, None])
    else:
        self._bias_tensor = bias_tensor
        self._vec_tensor = vec_tensor
    self._bias_embedding = bias_embedding
    self._vec_embedding = vec_embedding
    self._feature_columns = feature_columns
    self._frozen = False
    self._slot_ids = []
    self._feature_slots = {}
    self._feature_column_v1s = {}
    self._use_fid_v2 = False
    self._num_embedding_groups = 3