code: def __call__(self, x, dropout=False):
      if args.conv:
          x = dy.reshape(x, (28, 28, 1))
          x = dy.conv2d_bias(
              x, self.F1, self.b1, [1, 1], is_valid=False
          )
          x = dy.rectify(dy.maxpooling2d(x, [2, 2], [2, 2]))
          x = dy.conv2d_bias(
              x, self.F2, self.b2, [1, 1], is_valid=False
          )
          x = dy.rectify(dy.maxpooling2d(x, [2, 2], [2, 2]))  # 7x7x64
          x = dy.reshape(x, (7 * 7 * 64,))
      h = dy.rectify(self.W1 * x + self.hbias)
      if dropout:
          h = dy.dropout(h, DROPOUT_RATE)
      logits = self.W2 * h
      return logits
  
file_path: dynet/examples/mnist/mnist-autobatch.py
imports: import time
  import random
  import os
  import struct
  import argparse
  import numpy
  import dynet
label_desc: Use image node_id 2 for calling the MNISTClassify obj's underlying member method code with example usage: obj.__call__(x, dropout) and returns: logits