class_docstr: Data loader for the NextItNet model.
  
  NextItNet requires a special type of data format. In training stage, each instance will
  produce `(sequence_length * train_num_ngs)` target items and labels, to let NextItNet
  output predictions of every item in a sequence except only of the last item.
docstr: Initialize an iterator. Create necessary placeholders for the model.
  Different from sequential iterator
  
  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.
docstr_tok: ["Initialize","an","iterator",".","Create","necessary","placeholders","for","the","model",".","Different","from","sequential","iterator","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","."]
code: def __init__(self, hparams, graph, col_spliter="\t"):
      """Initialize an iterator. Create necessary placeholders for the model.
      Different from sequential iterator
  
      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.
      """
      self.col_spliter = col_spliter
  
      self.userdict, self.itemdict, self.catedict = (
          load_dict(hparams.user_vocab),
          load_dict(hparams.item_vocab),
          load_dict(hparams.cate_vocab),
      )
  
      self.max_seq_length = hparams.max_seq_length
      self.batch_size = hparams.batch_size
      self.iter_data = dict()
  
      self.graph = graph
      with self.graph.as_default():
          self.labels = tf.compat.v1.placeholder(
              tf.float32, [None, None], name="label"
          )
          self.users = tf.compat.v1.placeholder(
              tf.int32, [None], name="users"
          )
          self.items = tf.compat.v1.placeholder(
              tf.int32, [None, None], name="items"
          )
          self.cates = tf.compat.v1.placeholder(
              tf.int32, [None, None], name="cates"
          )
          self.item_history = tf.compat.v1.placeholder(
              tf.int32, [None, self.max_seq_length], name="item_history"
          )
          self.item_cate_history = tf.compat.v1.placeholder(
              tf.int32,
              [None, self.max_seq_length],
              name="item_cate_history",
          )
          self.mask = tf.compat.v1.placeholder(
              tf.int32, [None, self.max_seq_length], name="mask"
          )
          self.time = tf.compat.v1.placeholder(
              tf.float32, [None], name="time"
          )
          self.time_diff = tf.compat.v1.placeholder(
              tf.float32, [None, self.max_seq_length], name="time_diff"
          )
          self.time_from_first_action = tf.compat.v1.placeholder(
              tf.float32,
              [None, self.max_seq_length],
              name="time_from_first_action",
          )
          self.time_to_now = tf.compat.v1.placeholder(
              tf.float32,
              [None, self.max_seq_length],
              name="time_to_now",
          )
  
file_path: recommenders/recommenders/models/deeprec/io/nextitnet_iterator.py
imports: import tensorflow
  import numpy
  import random
  from recommenders.models.deeprec.io.sequential_iterator import SequentialIterator
  from recommenders.models.deeprec.deeprec_utils import load_dict
label_desc: Use image node_id 1 to create a new NextItNetIterator object from inherited base classes: SequentialIterator with example: obj = NextItNetIterator(hparams, graph, col_spliter)