class_docstr: Base class for sequential models
class_docstr_tok: ["Base","class","for","sequential","models"]
docstr: The main function to create sequential models.
  
  Returns:
      object: the prediction score make by the model.
docstr_tok: ["The","main","function","to","create","sequential","models",".","Returns",":","object",":","the","prediction","score","make","by","the","model","."]
code: def _build_graph(self):
      """The main function to create sequential models.
  
      Returns:
          object: the prediction score make by the model.
      """
      hparams = self.hparams
      self.keep_prob_train = 1 - np.array(hparams.dropout)
      self.keep_prob_test = np.ones_like(hparams.dropout)
  
      with tf.compat.v1.variable_scope(
          "sequential"
      ) as self.sequential_scope:
          self._build_embedding()
          self._lookup_from_embedding()
          model_output = self._build_seq_graph()
          logit = self._fcn_net(
              model_output, hparams.layer_sizes, scope="logit_fcn"
          )
          self._add_norm()
          return logit
  
file_path: recommenders/recommenders/models/deeprec/models/sequential/sequential_base_model.py
imports: import os
  import abc
  import numpy
  import tensorflow
  from recommenders.models.deeprec.models.base_model import BaseModel
  from recommenders.models.deeprec.deeprec_utils import cal_metric, load_dict
label_desc: Use image node_id 3 for calling the SequentialBaseModel obj's underlying member method code with example usage: obj._build_graph() and returns: logit