class_docstr: Trainer for an agreement model that always predicts that samples agree.
  
  The goal of this class is to simulate the behavior of the Neural Graph
  Machines model, which assumes that two nodes connected by a graph
  always have the same label.
class_docstr_tok: ["Trainer","for","an","agreement","model","that","always","predicts","that","samples","agree",".","The","goal","of","this","class","is","to","simulate","the","behavior","of","the","Neural","Graph","Machines","model",",","which","assumes","that","two","nodes","connected","by","a","graph","always","have","the","same","label","."]
code: def __init__(self, data, **unused_kwargs):
      self.data = data
      self.vars_to_save = []
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import logging
  import os
  from ..data.preprocessing import split_train_val
  import numpy
  import tensorflow
  from .trainer_base import batch_iterator
  from .trainer_base import Trainer
label_desc: Use image node_id 1 to create a new TrainerAgreementAlwaysAgree object from inherited base classes: object with example: obj = TrainerAgreementAlwaysAgree(data)