code: def build_network(self,
                        images,
                        phase_train=True,
                        nclass=1001,
                        image_depth=3,
                        data_type=tf.float32,
                        data_format="NCHW",
                        use_tf_layers=True,
                        fp16_vars=False):
          
          if data_format == "NCHW":
              images = tf.transpose(images, [0, 3, 1, 2])
          var_type = tf.float32
          if data_type == tf.float16 and fp16_vars:
              var_type = tf.float16
          network = convnet_builder.ConvNetBuilder(
              images, image_depth, phase_train, use_tf_layers, data_format,
              data_type, var_type)
          with tf.variable_scope(
                  "cg", custom_getter=network.get_custom_getter()):
              self.add_inference(network)
              # Add the final fully-connected class layer
              logits = (network.affine(nclass, activation="linear")
                        if not self.skip_final_affine_layer() else
                        network.top_layer)
              aux_logits = None
              if network.aux_top_layer is not None:
                  with network.switch_to_aux_top_layer():
                      aux_logits = network.affine(
                          nclass, activation="linear", stddev=0.001)
          if data_type == tf.float16:
              # TODO(reedwm): Determine if we should do this cast here.
              logits = tf.cast(logits, tf.float32)
              if aux_logits is not None:
                  aux_logits = tf.cast(aux_logits, tf.float32)
          return logits, aux_logits
docs: Returns logits and aux_logits from images.