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","."]
docstr: Creates the agreement prediction TensorFlow subgraph.
  
  This function is the equivalent of `create_agreement_prediction` in
  TrainerAgreement, but here we always predict 1.0.
  
  Args:
    src_indices: A Tensor or Placeholder of shape (batch_size,) containing the
      indices of the samples that are the sources of the edges.
    unused_args: Other unused arguments, which we allow in order to create a
      common interface with TrainerAgreement.
    unused_kwargs: Other unused keyword arguments, which we allow in order to
      create a common interface with TrainerAgreement.
  
  Returns:
    predictions: None, because this model doesn't do logits computations, but
      we still return something in order to keep the same function outputs as
      TrainerAgreement.
    normalized_predictions: A Tensor of shape (batch_size,) with values in
      {0, 1}, containing the agreement prediction probabilities.
    variables: An empty dictionary of trainable variables, because this model
      does not have any trainable variables.
    reg_params: An empty dictionary of variables that are used in the
      regularization weight decay term, because this model doesn't have
      regularization variables.
code: def create_agreement_prediction(
      self, src_indices, *unused_args, **unused_kwargs
  ):
      """Creates the agreement prediction TensorFlow subgraph.
  
      This function is the equivalent of `create_agreement_prediction` in
      TrainerAgreement, but here we always predict 1.0.
  
      Args:
        src_indices: A Tensor or Placeholder of shape (batch_size,) containing the
          indices of the samples that are the sources of the edges.
        unused_args: Other unused arguments, which we allow in order to create a
          common interface with TrainerAgreement.
        unused_kwargs: Other unused keyword arguments, which we allow in order to
          create a common interface with TrainerAgreement.
  
      Returns:
        predictions: None, because this model doesn't do logits computations, but
          we still return something in order to keep the same function outputs as
          TrainerAgreement.
        normalized_predictions: A Tensor of shape (batch_size,) with values in
          {0, 1}, containing the agreement prediction probabilities.
        variables: An empty dictionary of trainable variables, because this model
          does not have any trainable variables.
        reg_params: An empty dictionary of variables that are used in the
          regularization weight decay term, because this model doesn't have
          regularization variables.
      """
      return (
          None,
          tf.ones((tf.shape(src_indices)[0],), tf.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 4 for calling the TrainerAgreementAlwaysAgree obj's underlying member method code with example usage: obj.create_agreement_prediction(src_indices) and returns: None,  tf,  dict,  dict