def _input_placeholder():
 if (FLAGS.dataset == 'mnist'):
  image_size = 28
  num_channels = 1
 else:
  image_size = 32
  num_channels = 3
 train_node_shape = (FLAGS.batch_size, image_size, image_size, num_channels)
 return tf.placeholder(tf.float32, shape=train_node_shape)

def _input_placeholder ( ) : if ( FLAGS . dataset == 'mnist' ) : image_size = 28 num_channels = 1 else : image_size = 32 num_channels = 3 train_node_shape = ( FLAGS . batch_size , image_size , image_size , num_channels ) return tf . placeholder ( tf . float32 , shape = train_node_shape )
this helper function declares a tf placeholder for the graph input data :return: tf placeholder for the graph input data .