code: def train(
      self,
      batch_data,
      input_mapping_id="default",
      operations_id="default",
      losses_id="default",
      outputs_id="default",
  ):
      assert self._train, "Train is disabled"
      assert self._flag_isbuilt, "Train graph is not built"
  
      if input_mapping_id is None:
          feed_dict = {}
      else:
          feed_dict = self._generate_feed_dict(
              batch_data, self.T.get_input_mapping(input_mapping_id)
          )
  
      if operations_id is None:
          operations = []
      else:
          operations = self.T.get_operations(operations_id)
  
      if losses_id is None:
          losses = []
      else:
          losses = self.T.get_losses(losses_id)
  
      if outputs_id is None:
          outputs = []
      else:
          outputs = self.T.get_outputs(outputs_id)
  
      results = self._tf_train_sess.run(
          operations + losses + outputs, feed_dict=feed_dict
      )
  
      return_dict = {
          "losses": results[
              len(operations) : len(operations) + len(losses)
          ],
          "outputs": results[-len(outputs) :],
      }
  
      self._flag_updated = True
      return return_dict
  
imports: import tensorflow
  import numpy
  import os
label_desc: Use image node_id 3 for calling the Recommender obj's underlying member method code with example usage: obj.train(batch_data, input_mapping_id, operations_id, losses_id, outputs_id) and returns: return_dict