code: def neural_gpu_body(inputs, hparams, name=None):
    
    with tf.variable_scope(name, "neural_gpu"):
  
      def step(state, inp):  # pylint: disable=missing-docstring
        x = tf.nn.dropout(state, 1.0 - hparams.dropout)
        for layer in range(hparams.num_hidden_layers):
          x = common_layers.conv_gru(
              x, (hparams.kernel_height, hparams.kernel_width),
              hparams.hidden_size,
              name="cgru_%d" % layer)
        # Padding input is zeroed-out in the modality, we check this by summing.
        padding_inp = tf.less(tf.reduce_sum(tf.abs(inp), axis=[1, 2]), 0.00001)
        new_state = tf.where(padding_inp, state, x)  # No-op where inp is padding.
        return new_state
  
      return tf.foldl(
          step,
          tf.transpose(inputs, [1, 0, 2, 3]),
          initializer=inputs,
          parallel_iterations=1,
          swap_memory=True)
docs: The core Neural GPU.