class_docstr: Basic class of models
  
  Attributes:
      hparams (HParams): A HParams object, holds the entire set of hyperparameters.
      train_iterator (object): An iterator to load the data in training steps.
      test_iterator (object): An iterator to load the data in testing steps.
      graph (object): An optional graph.
      seed (int): Random seed.
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docstr: Go through the optimization step once with training data in feed_dict.
  
  Args:
      sess (object): The model session object.
      feed_dict (dict): Feed values to train the model. This is a dictionary that maps graph elements to values.
  
  Returns:
      list: A list of values, including update operation, total loss, data loss, and merged summary.
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code: def train(self, train_batch_data):
      """Go through the optimization step once with training data in feed_dict.
  
      Args:
          sess (object): The model session object.
          feed_dict (dict): Feed values to train the model. This is a dictionary that maps graph elements to values.
  
      Returns:
          list: A list of values, including update operation, total loss, data loss, and merged summary.
      """
      train_input, train_label = self._get_input_label_from_iter(
          train_batch_data
      )
      rslt = self.model.train_on_batch(train_input, train_label)
      return rslt
  
imports: import abc
  import time
  import numpy
  from tqdm import tqdm
  import tensorflow
  from tensorflow.compat.v1 import keras
  from recommenders.models.deeprec.deeprec_utils import cal_metric
label_desc: Use image node_id 8 for calling the BaseModel obj's underlying member method code with example usage: obj.train(train_batch_data) and returns: rslt