class_docstr: A very simple neural network
class_docstr_tok: ["A","very","simple","neural","network"]
code: def fprop(self, x, **kwargs):
      del kwargs
      with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
          w1 = tf.constant(
              [[1.5, 0.3], [-2, 0.3]], dtype=tf.as_dtype(x.dtype)
          )
          w2 = tf.constant(
              [[-2.4, 1.2], [0.5, -2.3]], dtype=tf.as_dtype(x.dtype)
          )
      h1 = tf.nn.sigmoid(tf.matmul(x, w1))
      res = tf.matmul(h1, w2)
      return {self.O_LOGITS: res, self.O_PROBS: tf.nn.softmax(res)}
  
imports: from functools import partial
  import unittest
  import numpy
  import tensorflow
  from cleverhans.devtools.checks import CleverHansTest
  from cleverhans.attacks_tf import fgm, pgd_attack, UnrolledAdam, UnrolledGradientDescent, parallel_apply_transformations
  from cleverhans.devtools.mocks import random_feed_dict
  from cleverhans.model import Model
label_desc: Use image node_id 2 for calling the SimpleModel obj's underlying member method code with example usage: obj.fprop(x) and returns: dict