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","."]
docstr: Predict agreement for the provided pairs of samples.
  
  The predictions are perfect according to the original dataset ground truth
  labels.
  The function contains many unused arguments, in order to conform with the
  interface of the TrainerAgreement class.
  
  Args:
    unused_session: A TensorFlow session where to run the model.
    unused_src_features: An array of shape (num_samples, num_features)
      containing the features of the first element of the pair.
    unused_tgt_features: An array of shape (num_samples, num_features)
      containing the features of the second element of the pair.
    src_indices: An array of integers containing the index of each sample in
      self.data of the samples in src_features.
    tgt_indices: An array of integers containing the index of each sample in
      self.data of the samples in tgt_features.
  
  Returns:
    An array containing the predicted agreement value for each pair of
    provided samples.
code: def predict(
      self,
      unused_session,
      unused_src_features,
      unused_tgt_features,
      src_indices,
      tgt_indices,
  ):
      """Predict agreement for the provided pairs of samples.
  
      The predictions are perfect according to the original dataset ground truth
      labels.
      The function contains many unused arguments, in order to conform with the
      interface of the TrainerAgreement class.
  
      Args:
        unused_session: A TensorFlow session where to run the model.
        unused_src_features: An array of shape (num_samples, num_features)
          containing the features of the first element of the pair.
        unused_tgt_features: An array of shape (num_samples, num_features)
          containing the features of the second element of the pair.
        src_indices: An array of integers containing the index of each sample in
          self.data of the samples in src_features.
        tgt_indices: An array of integers containing the index of each sample in
          self.data of the samples in tgt_features.
  
      Returns:
        An array containing the predicted agreement value for each pair of
        provided samples.
      """
      agreement = [
          self.data.get_original_labels(s)
          == self.data.get_original_labels(t)
          for s, t in zip(src_indices, tgt_indices)
      ]
      return np.asarray(agreement, dtype=np.float32)
  
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 3 for calling the TrainerPerfectAgreement obj's underlying member method code with example usage: obj.predict(unused_session, unused_src_features, unused_tgt_features, src_indices, tgt_indices) and returns: np