class_docstr: This loss function returns the root mean square error between the predictions and the true interactions, including
  all non-interacted values as 0s.
  Interactions can be any positive or negative values, and this loss function is sensitive to magnitude.
class_docstr_tok: ["This","loss","function","returns","the","root","mean","square","error","between","the","predictions","and","the","true","interactions",",","including","all","non-interacted","values","as","0s",".","Interactions","can","be","any","positive","or","negative","values",",","and","this","loss","function","is","sensitive","to","magnitude","."]
code: def connect_loss_graph(
      self, tf_interactions, tf_prediction, **kwargs
  ):
      error = tf.sparse_add(tf_interactions, -1.0 * tf_prediction)
      return tf.sqrt(tf.reduce_mean(tf.square(error)))
  
imports: import abc
  import tensorflow
label_desc: Use image node_id 1 for calling the RMSEDenseLossGraph obj's underlying member method code with example usage: obj.connect_loss_graph(tf_interactions, tf_prediction) and returns: tf