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: Returns the logits for the test items.
  
  Args:
      inputs (tf.Tensor): Input tensor.
  
  Returns:
       tf.Tensor: Output tensor.
docstr_tok: ["Returns","the","logits","for","the","test","items",".","Args",":","inputs","(","tf.Tensor",")",":","Input","tensor",".","Returns",":","tf.Tensor",":","Output","tensor","."]
code: def predict(self, inputs):
      """Returns the logits for the test items.
  
      Args:
          inputs (tf.Tensor): Input tensor.
  
      Returns:
           tf.Tensor: Output tensor.
      """
      training = False
      input_seq = inputs["input_seq"]
      candidate = inputs["candidate"]
  
      mask = tf.expand_dims(
          tf.cast(tf.not_equal(input_seq, 0), tf.float32), -1
      )
      seq_embeddings, positional_embeddings = self.embedding(input_seq)
      seq_embeddings += positional_embeddings
      # seq_embeddings = self.dropout_layer(seq_embeddings)
      seq_embeddings *= mask
      seq_attention = seq_embeddings
      seq_attention = self.encoder(seq_attention, training, mask)
      seq_attention = self.layer_normalization(
          seq_attention
      )  # (b, s, d)
      seq_emb = tf.reshape(
          seq_attention,
          [
              tf.shape(input_seq)[0] * self.seq_max_len,
              self.embedding_dim,
          ],
      )  # (b*s, d)
      candidate_emb = self.item_embedding_layer(candidate)  # (b, s, d)
      candidate_emb = tf.transpose(
          candidate_emb, perm=[0, 2, 1]
      )  # (b, d, s)
  
      test_logits = tf.matmul(seq_emb, candidate_emb)
      # (200, 100) * (1, 101, 100)'
  
      test_logits = tf.reshape(
          test_logits,
          [
              tf.shape(input_seq)[0],
              self.seq_max_len,
              1 + self.num_neg_test,
          ],
      )  # (1, 200, 101)
      test_logits = test_logits[:, -1, :]  # (1, 101)
      return test_logits
  
imports: import random
  import numpy
  from tqdm import tqdm
  import tensorflow
  from recommenders.utils.timer import Timer
label_desc: Use image node_id 4 for calling the SASREC obj's underlying member method code with example usage: obj.predict(inputs) and returns: test_logits