code: def __call__(self, inputs, dropout=False):
      x = dy.inputTensor(inputs)
      conv1 = dy.parameter(self.pConv1)
      b1 = dy.parameter(self.pB1)
      x = dy.conv2d_bias(x, conv1, b1, [1, 1], is_valid=False)
      x = dy.rectify(dy.maxpooling2d(x, [2, 2], [2, 2]))
      conv2 = dy.parameter(self.pConv2)
      b2 = dy.parameter(self.pB2)
      x = dy.conv2d_bias(x, conv2, b2, [1, 1], is_valid=False)
      x = dy.rectify(dy.maxpooling2d(x, [2, 2], [2, 2]))
      x = dy.reshape(x, (7 * 7 * 64, 1))
      w1 = dy.parameter(self.pW1)
      b3 = dy.parameter(self.pB3)
      h = dy.rectify(w1 * x + b3)
      if dropout:
          h = dy.dropout(h, DROPOUT_RATE)
      w2 = dy.parameter(self.pW2)
      output = w2 * h
      # output = dy.softmax(w2*h)
      return output
  
file_path: dynet/examples/mnist/basic-mnist-benchmarks/mnist_dynet_autobatch.py
imports: from __future__ import division
  import os
  import struct
  import argparse
  import random
  import time
  import numpy
  import dynet
label_desc: Use image node_id 2 for calling the mnist_network obj's underlying member method code with example usage: obj.__call__(inputs, dropout) and returns: output