class_docstr: The base trainer class. Given an object of `hparams`, a trainer
  creates and initializes a model. After initialization, the method
  `model_train` can be used to train the model.
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code: def _init_inputs(self):
      preproc_func = self.preproc_func
      input_shape = self.input_shape
      # Define input TF placeholder
      with tf.device("/gpu:0"):
          x_pre = tf.placeholder(
              tf.float32, shape=input_shape, name="x"
          )
          x = preprocess_batch(x_pre, preproc_func)
          y = tf.placeholder(
              tf.float32, shape=(self.batch_size, 10), name="y"
          )
  
      self.g0_inputs = {"x_pre": x_pre, "x": x, "y": y}
  
imports: from collections import OrderedDict
  import logging
  import math
  import time
  import os
  import numpy
  import six
  import tensorflow
  from cleverhans.utils_tf import batch_indices
  from cleverhans.utils_mnist import data_mnist
  import utils_cifar
  import utils_svhn
  from utils import preprocess_batch
  from make_model import make_model
  from evaluator import Evaluator
  from evaluator import create_adv_by_name
  from model import clone_variable
label_desc: Use image node_id 4 for calling the TrainManager obj's underlying member method code with example usage: obj._init_inputs() without return types