class_docstr: SAS Rec model
  Self-Attentive Sequential Recommendation Using Transformer
  
  :Citation:
  
      Wang-Cheng Kang, Julian McAuley (2018), Self-Attentive Sequential
      Recommendation. Proceedings of IEEE International Conference on
      Data Mining (ICDM'18)
  
      Original source code from nnkkmto/SASRec-tf2,
      https://github.com/nnkkmto/SASRec-tf2
docstr: function to create model inputs from sampled batch data.
  This function is used only during training.
docstr_tok: ["function","to","create","model","inputs","from","sampled","batch","data",".","This","function","is","used","only","during","training","."]
code: def create_combined_dataset(self, u, seq, pos, neg):
      """
      function to create model inputs from sampled batch data.
      This function is used only during training.
      """
      inputs = {}
      seq = tf.keras.preprocessing.sequence.pad_sequences(
          seq, padding="pre", truncating="pre", maxlen=self.seq_max_len
      )
      pos = tf.keras.preprocessing.sequence.pad_sequences(
          pos, padding="pre", truncating="pre", maxlen=self.seq_max_len
      )
      neg = tf.keras.preprocessing.sequence.pad_sequences(
          neg, padding="pre", truncating="pre", maxlen=self.seq_max_len
      )
  
      inputs["users"] = np.expand_dims(np.array(u), axis=-1)
      inputs["input_seq"] = seq
      inputs["positive"] = pos
      inputs["negative"] = neg
  
      target = np.concatenate(
          [
              np.repeat(1, seq.shape[0] * seq.shape[1]),
              np.repeat(0, seq.shape[0] * seq.shape[1]),
          ],
          axis=0,
      )
      target = np.expand_dims(target, axis=-1)
      return inputs, target
  
imports: import random
  import numpy
  from tqdm import tqdm
  import tensorflow
  from recommenders.utils.timer import Timer
label_desc: Use image node_id 6 for calling the SASREC obj's underlying member method code with example usage: obj.create_combined_dataset(u, seq, pos, neg) and returns: inputs,  target