class_docstr: Data loader for FFM format based models, such as xDeepFM.
  Iterator will not load the whole data into memory. Instead, it loads data into memory
  per mini-batch, so that large files can be used as input data.
docstr: Initialize an iterator. Create the necessary placeholders for the model.
  
  Args:
      hparams (object): Global hyper-parameters. Some key settings such as #_feature and #_field are there.
      graph (object): The running graph. All created placeholder will be added to this graph.
      col_spliter (str): column splitter in one line.
      ID_spliter (str): ID splitter in one line.
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code: def __init__(self, hparams, graph, col_spliter=" ", ID_spliter="%"):
      """Initialize an iterator. Create the necessary placeholders for the model.
  
      Args:
          hparams (object): Global hyper-parameters. Some key settings such as #_feature and #_field are there.
          graph (object): The running graph. All created placeholder will be added to this graph.
          col_spliter (str): column splitter in one line.
          ID_spliter (str): ID splitter in one line.
      """
      self.feature_cnt = hparams.FEATURE_COUNT
      self.field_cnt = hparams.FIELD_COUNT
      self.col_spliter = col_spliter
      self.ID_spliter = ID_spliter
      self.batch_size = hparams.batch_size
  
      self.graph = graph
      with self.graph.as_default():
          self.labels = tf.compat.v1.placeholder(
              tf.float32, [None, 1], name="label"
          )
          self.fm_feat_indices = tf.compat.v1.placeholder(
              tf.int64, [None, 2], name="fm_feat_indices"
          )
          self.fm_feat_values = tf.compat.v1.placeholder(
              tf.float32, [None], name="fm_feat_values"
          )
          self.fm_feat_shape = tf.compat.v1.placeholder(
              tf.int64, [None], name="fm_feat_shape"
          )
          self.dnn_feat_indices = tf.compat.v1.placeholder(
              tf.int64, [None, 2], name="dnn_feat_indices"
          )
          self.dnn_feat_values = tf.compat.v1.placeholder(
              tf.int64, [None], name="dnn_feat_values"
          )
          self.dnn_feat_weights = tf.compat.v1.placeholder(
              tf.float32, [None], name="dnn_feat_weights"
          )
          self.dnn_feat_shape = tf.compat.v1.placeholder(
              tf.int64, [None], name="dnn_feat_shape"
          )
  
file_path: recommenders/recommenders/models/deeprec/io/iterator.py
imports: import numpy
  import tensorflow
  import abc
label_desc: Use image node_id 1 to create a new FFMTextIterator object from inherited base classes: BaseIterator with example: obj = FFMTextIterator(hparams, graph, col_spliter, ID_spliter)