docstr: This function should build the model which takes the input variables
  and return cost at the end
code: def build_graph(self, image, label):
      """This function should build the model which takes the input variables
      and return cost at the end"""
  
      # In tensorflow, inputs to convolution function are assumed to be
      # NHWC. Add a single channel here.
      image = tf.expand_dims(image, 3)
  
      image = image * 2 - 1  # center the pixels values at zero
  
      # The context manager `argscope` sets the default option for all the layers under
      # this context. Here we use 32 channel convolution with shape 3x3
      with argscope(
          [tf.layers.conv2d], padding="same", activation=tf.nn.relu
      ):
          l = tf.layers.conv2d(image, 32, 3, name="conv0")
          l = tf.layers.max_pooling2d(l, 2, 2, padding="valid")
          l = tf.layers.conv2d(l, 32, 3, name="conv1")
          l = tf.layers.conv2d(l, 32, 3, name="conv2")
          l = tf.layers.max_pooling2d(l, 2, 2, padding="valid")
          l = tf.layers.conv2d(l, 32, 3, name="conv3")
          l = tf.layers.flatten(l)
          l = tf.layers.dense(l, 512, activation=tf.nn.relu, name="fc0")
          l = tf.layers.dropout(l, rate=0.5, training=self.training)
      logits = tf.layers.dense(
          l, 10, activation=tf.identity, name="fc1"
      )
  
      # a vector of length B with loss of each sample
      cost = tf.nn.sparse_softmax_cross_entropy_with_logits(
          logits=logits, labels=label
      )
      cost = tf.reduce_mean(
          cost, name="cross_entropy_loss"
      )  # the average cross-entropy loss
  
      correct = tf.cast(
          tf.nn.in_top_k(logits, label, 1), tf.float32, name="correct"
      )
      accuracy = tf.reduce_mean(correct, name="accuracy")
  
      # This will monitor training error & accuracy (in a moving average fashion). The value will be automatically
      # 1. written to tensosrboard
      # 2. written to stat.json
      # 3. printed after each epoch
      train_error = tf.reduce_mean(1 - correct, name="train_error")
      summary.add_moving_summary(train_error, accuracy)
  
      # Use a regex to find parameters to apply weight decay.
      # Here we apply a weight decay on all W (weight matrix) of all fc layers
      # If you don't like regex, you can certainly define the cost in any other methods.
      wd_cost = tf.multiply(
          1e-5,
          regularize_cost("fc.*/kernel", tf.nn.l2_loss),
          name="regularize_loss",
      )
      total_cost = tf.add_n([wd_cost, cost], name="total_cost")
      summary.add_moving_summary(cost, wd_cost, total_cost)
  
      # monitor histogram of all weight (of conv and fc layers) in tensorboard
      summary.add_param_summary((".*/kernel", ["histogram", "rms"]))
      # the function should return the total cost to be optimized
      return total_cost
  
file_path: tensorpack/examples/basics/mnist-tflayers.py
imports: from tensorpack import tfv1
  from tensorpack import *
  from tensorpack.dataflow import dataset
  from tensorpack.tfutils import summary
label_desc: Use image node_id 2 for calling the Model obj's underlying member method code with example usage: obj.build_graph(image, label) and returns: total_cost