class_docstr: Trainer for an agreement model that always predicts the correct value.
class_docstr_tok: ["Trainer","for","an","agreement","model","that","always","predicts","the","correct","value","."]
code: def __init__(self, data):
      self.data = data
      self.model = None
      self.vars_to_save = []
  
      # Save the true labels in a TensorFlow variable, which is used in the
      # create_agreement_prediction function.
      with tf.variable_scope("perfect_agreement"):
          indices = np.arange(data.num_samples)
          self.labels = tf.get_variable(
              "labels_original",
              initializer=self.data.get_original_labels(indices),
          )
          self.original_var_scope = tf.get_variable_scope()
  
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 TrainerPerfectAgreement object from inherited base classes: object with example: obj = TrainerPerfectAgreement(data)